Joseph Yuan-Chieh Lo
Professor in Radiology
My research uses computer vision and machine learning to improve medical imaging, focusing on breast and CT imaging. There are three specific projects:
(1) We design deep learning models to diagnose breast cancer from mammograms. We perform single-shot lesion detection, multi-task segmentation/classification, and image synthesis. Our goal is to improve radiologist diagnostic performance and empower patients to make personalized treatment decisions. This work is funded by NIH, Dept of Defense, Cancer Research UK, and other agencies.
(2) We create "digital twin" anatomical models that are based on actual patient data and thus contain highly realistic anatomy. With customized 3D printing, these virtual phantoms can also be rendered into physical form to be scanned on actual imaging devices, which allows us to assess image quality in new ways that are clinically relevant.
(3) We are building a computer-aided triage platform to classify multiple diseases across multiple organs in chest-abdomen-pelvis CT scans. Our hospital-scale data sets have hundreds of thousands of patients. This work includes natural language processing to analyze radiology reports as well as deep learning models for organ segmentation and disease classification.
Current Appointments & Affiliations
- Professor in Radiology, Radiology, Clinical Science Departments 2021
- Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2019
- Member of the Duke Cancer Institute, Duke Cancer Institute, Institutes and Centers 1993
Contact Information
- 2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705
- 2424 Erwin Road, Suite 302, Ravin Advanced Imaging Labs, Durham, NC 27705
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joseph.lo@duke.edu
(919) 684-7763
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Google Scholar
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Home Page
- Background
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Education, Training, & Certifications
- Research Associate, Radiology, Duke University 1993 - 1995
- Ph.D., Duke University 1993
- B.S.E.E., Duke University 1988
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Previous Appointments & Affiliations
- Professor of Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2014 - 2022
- Professor of Radiology, Radiology, Clinical Science Departments 2018 - 2020
- Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2014 - 2019
- Professor of Radiology, Radiology, Clinical Science Departments 2014 - 2018
- Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2014 - 2016
- Associate Research Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2011 - 2015
- Assistant Professor of Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2005 - 2014
- Associate Professor in the Department of Radiology, Radiology, Clinical Science Departments 2009 - 2014
- Assistant Professor in Radiology, Radiology, Clinical Science Departments 2006 - 2009
- Assistant Research Professor in Radiology, Radiology, Clinical Science Departments 1995 - 2006
- Assistant Research Professor in Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2003 - 2005
- Assistant Research Professor in Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 1997 - 1999
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Leadership & Clinical Positions at Duke
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Vice Chair for Research of Radiology
Associate Director, Medical Physics Graduate Program
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Vice Chair for Research of Radiology
- Recognition
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In the News
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JAN 20, 2022 Pratt School of Engineering
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- Expertise
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Subject Headings
- Breast Neoplasms
- Clinical Trials as Topic
- Computer Simulation
- Decision Making, Computer-Assisted
- Decision Support Systems, Clinical
- Decision Support Techniques
- Image Processing, Computer-Assisted
- Imaging, Three-Dimensional
- Machine learning
- Mammography
- Models, Structural
- Pattern Recognition, Automated
- Radiographic Image Interpretation, Computer-Assisted
- Radiology
- Technology Assessment, Biomedical
- Tomosynthesis
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Global Scholarship
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Expertise
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- Research
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Selected Grants
- Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications awarded by National Institutes of Health 2022 - 2027
- FW-HTF-R: Interpretable Machine Learning for Human-Machine Collaboration in High Stakes Decisions in Mammography awarded by National Science Foundation 2022 - 2026
- Duke Radiation Oncology and Radiology Stimulating Access to Research in Residency (Year 3) awarded by National Institutes of Health 2020 - 2024
- Prevent Ductal Carcinoma in Situ Invasive Overtreatment Now - PRECISION awarded by MD Anderson Cancer Center 2017 - 2023
- Multi-view Deep Learning to Improved Breast Cancer Detection for Digital Breast Tomosynthesis awarded by iCAD, Inc 2021 - 2023
- Machine learning and collaborative filtering tools for personalized education in digital breast tomosynthesis awarded by National Institutes of Health 2016 - 2022
- Breast Cancer Detection Consortium awarded by National Institutes of Health 2016 - 2022
- Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS awarded by Department of Defense 2014 - 2021
- 3D Printing of Anatomically Realistic Phantoms for Optimization of Imaging Algorithms awarded by National Institutes of Health 2018 - 2021
- Training in Medical Imaging awarded by National Institutes of Health 2003 - 2020
- (PQC3) Genomic Diversity and the Microenvironment as Drivers of Metastasis in DCIS awarded by National Institutes of Health 2014 - 2019
- Molecular and Radiologic Predictors of Invasion in a DCIS Active Surveillance Cohort awarded by Breast Cancer Research Foundation 2016 - 2018
- Improved education in digital breast tomosynthesis using machine learning and computer vision tools awarded by Radiological Society of North America 2014 - 2016
- (PQA5) 'Dose and Mechanisms of Exercise in Breast Cancer Prevention' awarded by National Institutes of Health 2013 - 2014
- 3D Digital Breast Phantoms For Multimodality Research awarded by National Institutes of Health 2010 - 2014
- Cross-disciplinary Training in Medical Physics awarded by National Institutes of Health 2007 - 2013
- Information-Theoretic Based CAD in Mammography awarded by National Institutes of Health 2003 - 2011
- Tomosynthesis for Improved Breast Cancer Detection awarded by National Institutes of Health 2006 - 2011
- Accurate Models for Predicting Radiation-Induced Injury awarded by National Institutes of Health 2006 - 2011
- Reducing Benign Breast Biopsies with Computer Modeling awarded by National Institutes of Health 2003 - 2008
- Predicting Breast Cancer With Ultrasound and Mammography awarded by National Institutes of Health 2002 - 2005
- Improved Diagnosis of Breast Microcalcification Clusters awarded by National Institutes of Health 2001 - 2004
- Computer-Aided Diagnosis Of Breast Cancer Invasion awarded by National Institutes of Health 1998 - 2003
- Computer Aid for the Decision to Biopsy Breast Lesions awarded by US Army Medical Research 1999 - 2002
- Computer Aid for the Decision to Biopsy Breast Lesions awarded by National Institutes of Health 1999 - 2001
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Zhang, D., Neely, B., Lo, J. Y., Patel, B. N., Hyslop, T., & Gupta, R. T. (2022). Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS. Acad Radiol. https://doi.org/10.1016/j.acra.2022.06.024Full Text Link to Item
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Hou, R., Peng, Y., Grimm, L. J., Ren, Y., Mazurowski, M. A., Marks, J. R., … Lo, J. Y. (2022). Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. Ieee Trans Biomed Eng, 69(5), 1639–1650. https://doi.org/10.1109/TBME.2021.3126281Full Text Link to Item
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D’Anniballe, V. M., Tushar, F. I., Faryna, K., Han, S., Mazurowski, M. A., Rubin, G. D., & Lo, J. Y. (2022). Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. Bmc Med Inform Decis Mak, 22(1), 102. https://doi.org/10.1186/s12911-022-01843-4Full Text Link to Item
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Hou, R., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., Maley, C. C., … Lo, J. Y. (2022). Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology, 303(1), 54–62. https://doi.org/10.1148/radiol.210407Full Text Link to Item
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Tong, H., Pegues, H., Samei, E., Lo, J. Y., & Wiley, B. J. (2022). Technical note: Controlling the attenuation of 3D-printed physical phantoms for computed tomography with a single material. Med Phys, 49(4), 2582–2589. https://doi.org/10.1002/mp.15494Full Text Link to Item
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Fu, W., Sharma, S., Abadi, E., Iliopoulos, A.-S., Wang, Q., Lo, J. Y., … Samei, E. (2022). Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry". Ieee J Biomed Health Inform, 26(1), 478. https://doi.org/10.1109/JBHI.2021.3130397Full Text Link to Item
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Tushar, F. I., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., Fu, W., Samei, E., … Lo, J. Y. (2022). Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell, 4(1), e210026. https://doi.org/10.1148/ryai.210026Full Text Link to Item
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Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y., & Rudin, C. (2021). A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nature Machine Intelligence, 3(12), 1061–1070. https://doi.org/10.1038/s42256-021-00423-xFull Text
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Fortunato, A., Mallo, D., Rupp, S. M., King, L. M., Hardman, T., Lo, J. Y., … Maley, C. C. (2021). A new method to accurately identify single nucleotide variants using small FFPE breast samples. Brief Bioinform, 22(6). https://doi.org/10.1093/bib/bbab221Full Text Link to Item
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Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Swiecicki, A., … Mazurowski, M. A. (2021). A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. Jama Netw Open, 4(8), e2119100. https://doi.org/10.1001/jamanetworkopen.2021.19100Full Text Link to Item
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Fu, W., Sharma, S., Abadi, E., Iliopoulos, A.-S., Wang, Q., Lo, J. Y., … Samei, E. (2021). iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry. Ieee Journal of Biomedical and Health Informatics, 25(8), 3061–3072. https://doi.org/10.1109/jbhi.2021.3063080Full Text
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Xue, C., Tang, F.-H., Lai, C. W. K., Grimm, L. J., & Lo, J. Y. (2021). Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change. Life (Basel), 11(8). https://doi.org/10.3390/life11080747Full Text Link to Item
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Grimm, L. J., Neely, B., Hou, R., Selvakumaran, V., Baker, J. A., Yoon, S. C., … Lo, J. Y. (2021). Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography. Ajr Am J Roentgenol, 216(4), 903–911. https://doi.org/10.2214/AJR.20.23679Full Text Link to Item
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Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y., & Rudin, C. (2021). IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography.Link to Item
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Ikejimba, L. C., Salad, J., Graff, C. G., Goodsitt, M., Chan, H.-P., Huang, H., … Glick, S. J. (2021). Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom. Med Phys, 48(3), 1026–1038. https://doi.org/10.1002/mp.14568Full Text Link to Item
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D’Anniballe, V. M., Tushar, F. I., Faryna, K., Han, S., Mazurowski, M. A., Rubin, G. D., & Lo, J. Y. (2021). Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning.Link to Item
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Draelos, R. L., Dov, D., Mazurowski, M. A., Lo, J. Y., Henao, R., Rubin, G. D., & Carin, L. (2021). Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Med Image Anal, 67, 101857. https://doi.org/10.1016/j.media.2020.101857Full Text Link to Item
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Draelos, R. L., Dov, D., Mazurowski, M. A., Lo, J. Y., Henao, R., Rubin, G. D., & Carin, L. (2021). Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical Image Anal., 67, 101857–101857. https://doi.org/10.1016/j.media.2020.101857Full Text
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Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Święcicki, A., … Mazurowski, M. A. (2020). Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. Jama Netw Open. 2021;4(8):E2119100.Link to Item
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Selvakumaran, V., Hou, R., Baker, J. A., Yoon, S. C., Ghate, S. V., Walsh, R., … Grimm, L. J. (2020). Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance. Acad Radiol, 27(11), 1580–1585. https://doi.org/10.1016/j.acra.2019.12.009Full Text Link to Item
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MacDonald, L. R., Lo, J. Y., Sturgeon, G. M., Zeng, C., Harrison, R. L., Kinahan, P. E., & Segars, W. P. (2020). Impact of Using Uniform Attenuation Coefficients for Heterogeneously Dense Breasts in a Dedicated Breast PET/X-ray Scanner. Ieee Trans Radiat Plasma Med Sci, 4(5), 585–593. https://doi.org/10.1109/trpms.2020.2991120Full Text Link to Item
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Fu, W., Sharma, S., Abadi, E., Iliopoulos, A.-S., Wang, Q., Lo, J. Y., … Samei, E. (2020). iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry.Link to Item
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Abadi, E., Segars, W. P., Tsui, B. M. W., Kinahan, P. E., Bottenus, N., Frangi, A. F., … Samei, E. (2020). Virtual clinical trials in medical imaging: a review. J Med Imaging (Bellingham), 7(4), 042805. https://doi.org/10.1117/1.JMI.7.4.042805Full Text Link to Item
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Hou, R., Mazurowski, M. A., Grimm, L. J., Marks, J. R., King, L. M., Maley, C. C., … Lo, J. Y. (2020). Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation. Ieee Trans Biomed Eng, 67(6), 1565–1572. https://doi.org/10.1109/TBME.2019.2940195Full Text Link to Item
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Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., … Jung, H. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. Jama Netw Open, 3(3), e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265Full Text Link to Item
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Ikejimba, L. C., Salad, J., Graff, C. G., Ghammraoui, B., Cheng, W.-C., Lo, J. Y., & Glick, S. J. (2019). A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom. Med Phys, 46(9), 3883–3892. https://doi.org/10.1002/mp.13629Full Text Link to Item
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Ren, Y., Zhu, Z., Li, Y., & Lo, J. (2019). Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images.Link to Item
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Grimm, L. J., Miller, M. M., Thomas, S. M., Liu, Y., Lo, J. Y., Hwang, E. S., … Ryser, M. D. (2019). Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease. Radiology, 292(1), 77–83. https://doi.org/10.1148/radiol.2019182599Full Text Link to Item
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Rossman, A. H., Catenacci, M., Zhao, C., Sikaria, D., Knudsen, J. E., Dawes, D., … Lo, J. Y. (2019). Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control region. J Med Imaging (Bellingham), 6(2), 021604. https://doi.org/10.1117/1.JMI.6.2.021604Full Text Link to Item
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Samei, E., & Lo, J. (2019). Special Section Guest Editorial: Special Section on 3D Printing in Medical Imaging. Journal of Medical Imaging, 6(2). https://doi.org/10.1117/1.JMI.6.2.021601Full Text
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Georgian-Smith, D., Obuchowski, N. A., Lo, J. Y., Brem, R. F., Baker, J. A., Fisher, P. R., … Mertelmeier, T. (2019). Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis. Ajr Am J Roentgenol, 1–7. https://doi.org/10.2214/AJR.18.20294Full Text Link to Item
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Hou, R., Ren, Y., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L., … Lo, J. Y. (2019). Malignant microcalcification clusters detection using unsupervised deep autoencoders. Progress in Biomedical Optics and Imaging Proceedings of Spie, 10950. https://doi.org/10.1117/12.2512829Full Text
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Shi, B., Grimm, L. J., Mazurowski, M. A., Baker, J. A., Marks, J. R., King, L. M., … Lo, J. Y. (2018). Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. J Am Coll Radiol, 15(3 Pt B), 527–534. https://doi.org/10.1016/j.jacr.2017.11.036Full Text Link to Item
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Wen, G., Chang, H.-C., Reinhold, J., Lo, J. Y., & Markey, M. K. (2018). Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images. J Med Imaging (Bellingham), 5(1), 015501. https://doi.org/10.1117/1.JMI.5.1.015501Full Text Link to Item
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Sturgeon, G. M., Park, S., Segars, W. P., & Lo, J. Y. (2017). Synthetic breast phantoms from patient based eigenbreasts. Med Phys, 44(12), 6270–6279. https://doi.org/10.1002/mp.12579Full Text Link to Item
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Shi, B., Grimm, L. J., Mazurowski, M. A., Baker, J. A., Marks, J. R., King, L. M., … Lo, J. Y. (2017). Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol, 24(9), 1139–1147. https://doi.org/10.1016/j.acra.2017.03.013Full Text Link to Item
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Ikejimba, L. C., Graff, C. G., Rosenthal, S., Badal, A., Ghammraoui, B., Lo, J. Y., & Glick, S. J. (2017). A novel physical anthropomorphic breast phantom for 2D and 3D x-ray imaging. Med Phys, 44(2), 407–416. https://doi.org/10.1002/mp.12062Full Text Link to Item
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Ikejimba, L. C., Glick, S. J., Choudhury, K. R., Samei, E., & Lo, J. Y. (2016). Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms. Med Phys, 43(10), 5593. https://doi.org/10.1118/1.4962475Full Text Link to Item
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Wang, M., Zhang, J., Grimm, L. J., Ghate, S. V., Walsh, R., Johnson, K. S., … Mazurowski, M. A. (2016). Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. Expert Systems With Applications, 56, 1–8. https://doi.org/10.1016/j.eswa.2016.01.053Full Text
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Kiarashi, N., Nolte, L. W., Lo, J. Y., Segars, W. P., Ghate, S. V., Solomon, J. B., & Samei, E. (2016). Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study. J Med Imaging (Bellingham), 3(3), 035504. https://doi.org/10.1117/1.JMI.3.3.035504Full Text Link to Item
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Sturgeon, G. M., Kiarashi, N., Lo, J. Y., Samei, E., & Segars, W. P. (2016). Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation. Med Phys, 43(5), 2207. https://doi.org/10.1118/1.4945275Full Text Link to Item
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Ikejimba, L., Lo, J. Y., Chen, Y., Oberhofer, N., Kiarashi, N., & Samei, E. (2016). A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom. Med Phys, 43(4), 1627. https://doi.org/10.1118/1.4943373Full Text Link to Item
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Grimm, L. J., Zhang, J., Lo, J. Y., Johnson, K. S., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2016). Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns. J Am Coll Radiol, 13(2), 198–202. https://doi.org/10.1016/j.jacr.2015.09.025Full Text Link to Item
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Erickson, D. W., Wells, J. R., Sturgeon, G. M., Samei, E., Dobbins, J. T., Segars, W. P., & Lo, J. Y. (2016). Population of 224 realistic human subject-based computational breast phantoms. Med Phys, 43(1), 23. https://doi.org/10.1118/1.4937597Full Text Link to Item
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Schmidt, M., Lo, J. Y., Grzetic, S., Lutzky, C., Brizel, D. M., & Das, S. K. (2015). Semiautomated head-and-neck IMRT planning using dose warping and scaling to robustly adapt plans in a knowledge database containing potentially suboptimal plans. Med Phys, 42(8), 4428–4434. https://doi.org/10.1118/1.4923174Full Text Link to Item
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Kiarashi, N., Nolte, A. C., Sturgeon, G. M., Segars, W. P., Ghate, S. V., Nolte, L. W., … Lo, J. Y. (2015). Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data. Med Phys, 42(7), 4116–4126. https://doi.org/10.1118/1.4919771Full Text Link to Item
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Zhang, J., Grimm, L. J., Lo, J. Y., Johnson, K. S., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2015). Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis? J Am Coll Radiol, 12(7), 728–732. https://doi.org/10.1016/j.jacr.2015.02.025Full Text Link to Item
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Sechopoulos, I., Sabol, J. M., Berglund, J., Bolch, W. E., Brateman, L., Christodoulou, E., … Von Tiedemann, M. (2014). Radiation dosimetry in digital breast tomosynthesis: report of AAPM Tomosynthesis Subcommittee Task Group 223. Med Phys, 41(9), 091501. https://doi.org/10.1118/1.4892600Full Text Link to Item
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Zhang, J., Lo, J. Y., Kuzmiak, C. M., Ghate, S. V., Yoon, S. C., & Mazurowski, M. A. (2014). Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents. Med Phys, 41(9), 091907. https://doi.org/10.1118/1.4892173Full Text Link to Item
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Kiarashi, N., Lo, J. Y., Lin, Y., Ikejimba, L. C., Ghate, S. V., Nolte, L. W., … Samei, E. (2014). Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems. Ieee Trans Med Imaging, 33(7), 1401–1409. https://doi.org/10.1109/TMI.2014.2312733Full Text Link to Item
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Good, D., Lo, J., Lee, W. R., Wu, Q. J., Yin, F.-F., & Das, S. K. (2013). A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. Int J Radiat Oncol Biol Phys, 87(1), 176–181. https://doi.org/10.1016/j.ijrobp.2013.03.015Full Text Link to Item
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Huo, Z., Summers, R. M., Paquerault, S., Lo, J., Hoffmeister, J., Armato, S. G., … Chan, H.-P. (2013). Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use. Med Phys, 40(7), 077001. https://doi.org/10.1118/1.4807642Full Text Link to Item
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Chung, H., Ikejimba, L., Kiarashi, N., Samei, E., Hoernig, M., & Lo, J. Y. (2013). Estimating breast density with dual energy mammography: A simple model based on calibration phantoms. Progress in Biomedical Optics and Imaging Proceedings of Spie, 8668. https://doi.org/10.1117/12.2008398Full Text
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Kiarashi, N., Ghate, S. V., Lo, J. Y., Nolte, L. W., & Samei, E. (2012). Application of a dynamic 4D anthropomorphic breast phantom in contrast-based imaging system optimization: Dual-energy or temporal subtraction? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7361 LNCS, 658–665. https://doi.org/10.1007/978-3-642-31271-7_85Full Text
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Baker, J. A., & Lo, J. Y. (2011). Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol, 18(10), 1298–1310. https://doi.org/10.1016/j.acra.2011.06.011Full Text Link to Item
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Mazurowski, M. A., Lo, J. Y., Harrawood, B. P., & Tourassi, G. D. (2011). Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis. J Biomed Inform, 44(5), 815–823. https://doi.org/10.1016/j.jbi.2011.04.008Full Text Link to Item
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Shafer, C. M., Seewaldt, V. L., & Lo, J. Y. (2011). Validation of a 3D hidden-Markov model for breast tissue segmentation and density estimation from MR and tomosynthesis images. Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, Bsec 2011. https://doi.org/10.1109/BSEC.2011.5872317Full Text
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Chanyavanich, V., Das, S. K., Lee, W. R., & Lo, J. Y. (2011). Knowledge-based IMRT treatment planning for prostate cancer. Med Phys, 38(5), 2515–2522. https://doi.org/10.1118/1.3574874Full Text Link to Item
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Webb, L. J., Samei, E., Lo, J. Y., Baker, J. A., Ghate, S. V., Kim, C., … Walsh, R. (2011). Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection. Med Phys, 38(4), 1972–1980. https://doi.org/10.1118/1.3562901Full Text Link to Item
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Singh, S., Maxwell, J., Baker, J. A., Nicholas, J. L., & Lo, J. Y. (2011). Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology, 258(1), 73–80. https://doi.org/10.1148/radiol.10081308Full Text Link to Item
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Robinson, M. D., Toth, C. A., Lo, J. Y., & Farsiu, S. (2010). Efficient fourier-wavelet super-resolution. Ieee Trans Image Process, 19(10), 2669–2681. https://doi.org/10.1109/TIP.2010.2050107Full Text Link to Item
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Ranger, N. T., Lo, J. Y., & Samei, E. (2010). A technique optimization protocol and the potential for dose reduction in digital mammography. Med Phys, 37(3), 962–969. https://doi.org/10.1118/1.3276732Full Text Link to Item
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Shafer, C. M., Samei, E., & Lo, J. Y. (2010). The quantitative potential for breast tomosynthesis imaging. Med Phys, 37(3), 1004–1016. https://doi.org/10.1118/1.3285038Full Text Link to Item
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Chawla, A. S., Lo, J. Y., Baker, J. A., & Samei, E. (2009). Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. Med Phys, 36(11), 4859–4869. https://doi.org/10.1118/1.3231814Full Text Link to Item
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Saunders, R. S., Samei, E., Lo, J. Y., & Baker, J. A. (2009). Can compression be reduced for breast tomosynthesis? Monte carlo study on mass and microcalcification conspicuity in tomosynthesis. Radiology, 251(3), 673–682. https://doi.org/10.1148/radiol.2521081278Full Text Link to Item
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Jesneck, J. L., Mukherjee, S., Yurkovetsky, Z., Clyde, M., Marks, J. R., Lokshin, A. E., & Lo, J. Y. (2009). Do serum biomarkers really measure breast cancer? Bmc Cancer, 9, 164. https://doi.org/10.1186/1471-2407-9-164Full Text Link to Item
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Chawla, A. S., Saunders, R. S., Singh, S., Lo, J. Y., & Samei, E. (2009). Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer. Acad Radiol, 16(4), 456–463. https://doi.org/10.1016/j.acra.2008.09.013Full Text Link to Item
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Singh, S., Tourassi, G. D., Baker, J. A., Samei, E., & Lo, J. Y. (2008). Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach. Med Phys, 35(8), 3626–3636. https://doi.org/10.1118/1.2953562Full Text Link to Item
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Williams, M. B., Raghunathan, P., More, M. J., Seibert, J. A., Kwan, A., Lo, J. Y., … Mawdsley, G. E. (2008). Optimization of exposure parameters in full field digital mammography. Med Phys, 35(6), 2414–2423. https://doi.org/10.1118/1.2912177Full Text Link to Item
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Floyd, C. E., Kapadia, A. J., Bender, J. E., Sharma, A. C., Xia, J. Q., Harrawood, B. P., … Howell, C. R. (2008). Neutron-stimulated emission computed tomography of a multi-element phantom. Phys Med Biol, 53(9), 2313–2326. https://doi.org/10.1088/0031-9155/53/9/008Full Text Link to Item
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Xia, J. Q., Lo, J. Y., Yang, K., Floyd, C. E., & Boone, J. M. (2008). Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique. Med Phys, 35(5), 1950–1958. https://doi.org/10.1118/1.2903436Full Text Link to Item
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Chawla, A. S., Samei, E., Saunders, R. S., Lo, J. Y., & Baker, J. A. (2008). A mathematical model platform for optimizing a multiprojection breast imaging system. Med Phys, 35(4), 1337–1345. https://doi.org/10.1118/1.2885367Full Text Link to Item
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Karellas, A., Lo, J. Y., & Orton, C. G. (2008). Point/Counterpoint. Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer. Medical Physics, 35(2), 409–411. https://doi.org/10.1118/1.2825612Full Text
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Chen, Y., Lo, J. Y., & Dobbins, J. T. (2008). Impulse response and Modulation Transfer Function analysis for Shift-And-Add and Back Projection image reconstruction algorithms in Digital Breast Tomosynthesis (DBT). Int J Funct Inform Personal Med, 1(2), 189–204. https://doi.org/10.1504/IJFIPM.2008.020187Full Text Link to Item
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Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., & Tourassi, G. D. (2008). Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw, 21(2–3), 427–436. https://doi.org/10.1016/j.neunet.2007.12.031Full Text Link to Item
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Chen, Y., Lo, J. Y., & Dobbins, J. T. (2007). A comparison between traditional shift-and-add (SAA) and point-by-point back projection (BP) - Relevance to morphology of microcalcifications for isocentric motion in Digital Breast tomosynthesis (DBT). Proceedings of the 7th Ieee International Conference on Bioinformatics and Bioengineering, Bibe, 563–569. https://doi.org/10.1109/BIBE.2007.4375617Full Text
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Singh, S., Tourassi, G. D., & Lo, J. Y. (2007). Breast mass detection in tomosynthesis projection images using information-theoretic similarity measures. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6514(PART 1). https://doi.org/10.1117/12.713004Full Text
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Chen, Y., Lo, J. Y., & Dobbins, J. T. (2007). Importance of point-by-point back projection correction for isocentric motion in digital breast tomosynthesis: relevance to morphology of structures such as microcalcifications. Med Phys, 34(10), 3885–3892. https://doi.org/10.1118/1.2776256Full Text Link to Item
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Jesneck, J. L., Lo, J. Y., & Baker, J. A. (2007). Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors. Radiology, 244(2), 390–398. https://doi.org/10.1148/radiol.2442060712Full Text Link to Item
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Tourassi, G. D., Harrawood, B., Singh, S., & Lo, J. Y. (2007). Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance. Med Phys, 34(8), 3193–3204. https://doi.org/10.1118/1.2751075Full Text Link to Item
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Baydush, A. H., Catarious, D. M., Lo, J. Y., & Floyd, C. E. (2007). Incorporation of a Laguerre-Gauss channelized Hotelling observer for false-positive reduction in a mammographic mass CAD system. J Digit Imaging, 20(2), 196–202. https://doi.org/10.1007/s10278-007-9009-8Full Text Link to Item
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Samei, E., Stebbins, S. A., Dobbins, J. T., McAdams, H. P., & Lo, J. Y. (2007). Multiprojection correlation imaging for improved detection of pulmonary nodules. Ajr Am J Roentgenol, 188(5), 1239–1245. https://doi.org/10.2214/AJR.06.0843Full Text Link to Item
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Patterson, K., Vasek, J., Yuan, C. M., Bailey, G. E., Kusnadi, I., Do, T., & Sturtevant, J. L. (2007). Circuit-based SEM contour OPC model calibration. Spie Proceedings. https://doi.org/10.1117/12.713044Full Text
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Floyd, C. E., Sharma, A. C., Bender, J. E., Kapadia, A. J., Xia, J. Q., Harrawood, B. P., … Howell, C. R. (2007). Neutron stimulated emission computed tomography: Background corrections. Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions With Materials and Atoms, 254(2), 329–336. https://doi.org/10.1016/j.nimb.2006.11.098Full Text
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Tourassi, G. D., Harrawood, B., Singh, S., Lo, J. Y., & Floyd, C. E. (2007). Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. Med Phys, 34(1), 140–150. https://doi.org/10.1118/1.2401667Full Text Link to Item
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Jesneck, J. L., Nolte, L. W., Baker, J. A., Floyd, C. E., & Lo, J. Y. (2006). Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys, 33(8), 2945–2954. https://doi.org/10.1118/1.2208934Full Text Link to Item
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Floyd, C. E., Bender, J. E., Sharma, A. C., Kapadia, A., Xia, J., Harrawood, B., … Howell, C. (2006). Introduction to neutron stimulated emission computed tomography. Phys Med Biol, 51(14), 3375–3390. https://doi.org/10.1088/0031-9155/51/14/006Full Text Link to Item
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Sharma, A., Floyd, C., Harrawood, B., Tourassi, G., Kapadia, A., Bender, J., … Howell, C. (2006). Rotating slat collimator design for high-energy near-field imaging. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6142 I. https://doi.org/10.1117/12.653929Full Text
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Chen, Y., Lo, J. Y., & Dobbins, J. T. (2006). Noise power spectrum analysis for several digital breast tomosynthesis reconstruction algorithms. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6142 III. https://doi.org/10.1117/12.652282Full Text
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Chen, Y., Lo, J. Y., Baker, J. A., & Dobbins, J. T. (2006). Gaussian frequency blending algorithm with Matrix Inversion Tomosynthesis (MITS) and Filtered Back Projection (FBP) for better digital breast tomosynthesis reconstruction. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6142 I. https://doi.org/10.1117/12.652264Full Text
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Floyd, C. E., Bender, J. E., Harrawood, B., Sharma, A. C., Kapadia, A., Tourassi, G. D., … Howell, C. (2006). Breast cancer diagnosis using neutron stimulated emission computed tomography: Dose and count requirements. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6142 II. https://doi.org/10.1117/12.656045Full Text
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Jesneck, J. L., Nolte, L. W., Baker, J. A., & Lo, J. Y. (2006). The effect of data set size on computer-aided diagnosis of breast cancer: Comparing decision fusion to a linear discriminant. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6146. https://doi.org/10.1117/12.655235Full Text
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Singh, S., Baydush, A., Harrawood, B., & Lo, J. (2006). Mass detection in mammographic ROIs using Watson filters. Progress in Biomedical Optics and Imaging Proceedings of Spie, 6146. https://doi.org/10.1117/12.653224Full Text
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Williams, M. B., Raghunathan, P., Seibert, A., Kwan, A., Lo, J., Samei, E., … Bloomquist, A. (2006). Beam optimization for digital mammography - II. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4046 LNCS, 273–280. https://doi.org/10.1007/11783237_38Full Text
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Chen, Y., Lo, J. Y., & Dobbins, J. T. (2005). Impulse response analysis for several digital tomosynthesis mammography reconstruction algorithms. Progress in Biomedical Optics and Imaging Proceedings of Spie, 5745(I), 541–549. https://doi.org/10.1117/12.595684Full Text
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Jesneck, J. L., Saunders, R. S., Samei, E., Xia, J. Q., & Lo, J. Y. (2005). Detector evaluation of a prototype amorphous selenium-based full field digital mammography system. Progress in Biomedical Optics and Imaging Proceedings of Spie, 5745(I), 478–485. https://doi.org/10.1117/12.596087Full Text
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Markey, M. K., & Lo, J. Y. (2005). Issues in assessing multi-institutional performance of BI-RADS-based CAD systems. Progress in Biomedical Optics and Imaging Proceedings of Spie, 5747(II), 858–865. https://doi.org/10.1117/12.594706Full Text
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Xia, J. Q., Lo, J. Y., & Floyd, C. E. (2005). Characterization of scatter radiation of a breast phantom on siemens prototype FFDM with and without an anti-scatter grid. Progress in Biomedical Optics and Imaging Proceedings of Spie, 5745(II), 1096–1102. https://doi.org/10.1117/12.595960Full Text
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Samei, E., Lo, J. Y., Yoshizumi, T. T., Jesneck, J. L., Dobbins, J. T., Floyd, C. E., … Ravin, C. E. (2005). Comparative scatter and dose performance of slot-scan and full-field digital chest radiography systems. Radiology, 235(3), 940–949. https://doi.org/10.1148/radiol.2353040516Full Text Link to Item
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Bilska-Wolak, A. O., Floyd, C. E., Lo, J. Y., & Baker, J. A. (2005). Computer aid for decision to biopsy breast masses on mammography: validation on new cases. Acad Radiol, 12(6), 671–680. https://doi.org/10.1016/j.acra.2005.02.011Full Text Link to Item
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Baker, J. A., Rosen, E. L., Crockett, M. M., & Lo, J. Y. (2005). Accuracy of segmentation of a commercial computer-aided detection system for mammography. Radiology, 235(2), 385–390. https://doi.org/10.1148/radiol.2352040899Full Text Link to Item
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Saunders, R. S., Samei, E., Jesneck, J. L., & Lo, J. Y. (2005). Physical characterization of a prototype selenium-based full field digital mammography detector. Med Phys, 32(2), 588–599. https://doi.org/10.1118/1.1855033Full Text Link to Item
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Samei, E., Dobbins, J. T., Lo, J. Y., & Tornai, M. P. (2005). A framework for optimising the radiographic technique in digital X-ray imaging. Radiat Prot Dosimetry, 114(1–3), 220–229. https://doi.org/10.1093/rpd/nch562Full Text Link to Item
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Bissonnette, M., Hansroul, M., Masson, E., Savard, S., Cadieux, S., Warmoes, P., … Singh, S. (2005). Digital breast tomosynthesis using an amorphous selenium flat panel detector. Progress in Biomedical Optics and Imaging Proceedings of Spie, 5745(I), 529–540. https://doi.org/10.1117/12.601622Full Text Link to Item
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Fischer, E. A., Lo, J. Y., & Markey, M. K. (2004). Bayesian networks of BI-RADS™ descriptors for breast lesion Classification. Annual International Conference of the Ieee Engineering in Medicine and Biology Proceedings, 26 IV, 3031–3034.
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Baker, J. A., Lo, J. Y., Delong, D. M., & Floyd, C. E. (2004). Computer-aided detection in screening mammography: variability in cues. Radiology, 233(2), 411–417. https://doi.org/10.1148/radiol.2332031200Full Text Link to Item
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Land, W. H., McKee, D. W., Anderson, F. R., & Lo, J. Y. (2004). Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines. Proceedings of Spie the International Society for Optical Engineering, 5370 II, 880–887. https://doi.org/10.1117/12.535864Full Text
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Land, W. H., Wong, L., McKee, D., Masters, T., Anderson, F., Raturi, A., & Lo, J. Y. (2004). New results in computer aided diagnosis (CAD) of breast cancer using a recently developed SVM/GRNN oracle hybrid. Proceedings of Spie the International Society for Optical Engineering, 5370 II, 777–784. https://doi.org/10.1117/12.533142Full Text
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Samei, E., Saunders, R. S., Lo, J. Y., Dobbins, J. T., Jesneck, J. L., Floyd, C. E., & Ravin, C. E. (2004). Fundamental imaging characteristics of a slot-scan digital chest radiographic system. Med Phys, 31(9), 2687–2698. https://doi.org/10.1118/1.1783531Full Text Link to Item
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Baker, J. A., Rosen, E. L., Lo, J. Y., Gimenez, E. I., Walsh, R., & Soo, M. S. (2003). Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. Ajr Am J Roentgenol, 181(4), 1083–1088. https://doi.org/10.2214/ajr.181.4.1811083Full Text Link to Item
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Bilska-Wolak, A. O., Floyd, C. E., & Lo, J. Y. (2003). Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers. Proceedings of Spie the International Society for Optical Engineering, 5032 III, 1386–1391. https://doi.org/10.1117/12.481349Full Text
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Land, W. H., McKee, D. W., Lo, J. Y., & Anderson, F. R. (2003). Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions. Proceedings of Spie the International Society for Optical Engineering, 5032 II, 898–907. https://doi.org/10.1117/12.483550Full Text
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Land, W. H., McKee, D., Velazquez, R., Wong, L., Lo, J. Y., & Anderson, F. (2003). Application of support vector machines to breast cancer screening using mammogram and clinical history data. Proceedings of Spie the International Society for Optical Engineering, 5032 I, 546–556. https://doi.org/10.1117/12.480235Full Text
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Lo, J. Y., Gavrielides, M., Markey, M. K., & Jesneck, J. L. (2003). Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists. Proceedings of Spie the International Society for Optical Engineering, 5032 II, 882–889. https://doi.org/10.1117/12.480869Full Text
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Tourassi, G. D., Lo, J. Y., & Markey, M. K. (2003). Validation of a constraint satisfaction neural network for breast cancer diagnosis: New results from 1,030 cases. Proceedings of Spie the International Society for Optical Engineering, 5032 I, 207–214. https://doi.org/10.1117/12.481111Full Text
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Bilska-Wolak, A. O., Floyd, C. E., Nolte, L. W., & Lo, J. Y. (2003). Application of likelihood ratio to classification of mammographic masses; performance comparison to case-based reasoning. Med Phys, 30(5), 949–958. https://doi.org/10.1118/1.1565339Full Text Link to Item
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Markey, M. K., Lo, J. Y., Tourassi, G. D., & Floyd, C. E. (2003). Self-organizing map for cluster analysis of a breast cancer database. Artif Intell Med, 27(2), 113–127. https://doi.org/10.1016/s0933-3657(03)00003-4Full Text Link to Item
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Land, W. H., Lo, J. Y., & Velázquez, R. (2002). Using evolutionary programming to configure support vector machines for the diagnosis of breast cancer. Intelligent Engineering Systems Through Artificial Neural Networks, 12, 249–254.
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Land, W. H., McKee, D. W., Lo, J. Y., & Anderson, F. (2002). Improving mammogram screening using a bank of support vector machines (SVMs). Intelligent Engineering Systems Through Artificial Neural Networks, 12, 779–784.
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Markey, M. K., Lo, J. Y., & Floyd, C. E. (2002). Differences between computer-aided diagnosis of breast masses and that of calcifications. Radiology, 223(2), 489–493. https://doi.org/10.1148/radiol.2232011257Full Text Link to Item
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Gavrielides, M. A., Lo, J. Y., & Floyd, C. E. (2002). Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. Med Phys, 29(4), 475–483. https://doi.org/10.1118/1.1460874Full Text Link to Item
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Keogan, M. T., Lo, J. Y., Freed, K. S., Raptopoulos, V., Blake, S., Kamel, I. R., … Nelson, R. C. (2002). Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol, 9(4), 410–419. https://doi.org/10.1016/s1076-6332(03)80186-1Full Text Link to Item
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Markey, M. K., Lo, J. Y., Vargas-Voracek, R., Tourassi, G. D., & Floyd, C. E. (2002). Perceptron error surface analysis: a case study in breast cancer diagnosis. Comput Biol Med, 32(2), 99–109. https://doi.org/10.1016/s0010-4825(01)00035-xFull Text Link to Item
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Lo, J. Y., Markey, M. K., Baker, J. A., & Floyd, C. E. (2002). Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer. Ajr Am J Roentgenol, 178(2), 457–463. https://doi.org/10.2214/ajr.178.2.1780457Full Text Link to Item
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Land, W. H., Akanda, A., Lo, J. Y., Anderson, F., & Bryden, M. (2002). Application of support vector machines to breast cancer screening using mammogram and history data. Proceedings of Spie the International Society for Optical Engineering, 4684 I, 636–642. https://doi.org/10.1117/12.467206Full Text
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Land, W. H., Bryden, M., Lo, J. Y., McKee, D. W., & Anderson, F. R. (2002). Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms. Proceedings of the 2002 Congress on Evolutionary Computation, Cec 2002, 1, 187–192. https://doi.org/10.1109/CEC.2002.1006231Full Text
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Markey, M. K., Lo, J. Y., Tourassi, G. D., & Floyd, C. E. (2002). Cluster analysis of BI-RADS™ descriptions of biopsy-proven breast lesions. Proceedings of Spie the International Society for Optical Engineering, 4684 I, 363–370. https://doi.org/10.1117/12.467177Full Text
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Baydush, A. H., Catarious, D. M., Lo, J. Y., Abbey, C. K., & Floyd, C. E. (2001). Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers. Medical Physics, 28(12), 2403–2409. https://doi.org/10.1118/1.1420402Full Text
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Tourassi, G. D., Markey, M. K., Lo, J. Y., & Floyd, C. E. (2001). A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys, 28(5), 804–811. https://doi.org/10.1118/1.1367861Full Text Link to Item
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Land, J., Masters, T., Lo, J. Y., & McKee, D. W. (2001). Application of adaptive boosting to EP-derived multi-layer feedforward neural networks (MLFN) to improve benign/malignant breast cancer classification. Proceedings of Spie the International Society for Optical Engineering, 4322(3), 1717–1724. https://doi.org/10.1117/12.431058Full Text
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Land, J., Masters, T., Lo, J. Y., & McKee, D. W. (2001). Application of evolutionary computation and neural network hybrids for breast cancer classification using mammogram and history data. Proceedings of the Ieee Conference on Evolutionary Computation, Icec, 2, 1147–1154.
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Land, W., Masters, T., & Lo, J. (2000). Application of a new Evolutionary Programming/Adaptive Boosting hybrid to breast cancer diagnosis. Proceedings of the Ieee Conference on Evolutionary Computation, Icec, 2, 1436–1442.
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Floyd, C. E., Lo, J. Y., & Tourassi, G. D. (2000). Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. Ajr. American Journal of Roentgenology, 175(5), 1347–1352. https://doi.org/10.2214/ajr.175.5.1751347Full Text
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Tourassi, G. D., Floyd, Jr., C. E., & Lo, J. Y. (2000). <title>Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation</title>. Spie Proceedings. https://doi.org/10.1117/12.387680Full Text
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Jr, W. H. L., Masters, T., & Lo, J. Y. (2000). Application of a GRNN ORACLE to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Proceedings of Spie the International Society for Optical Engineering, 3979, I/-.
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Lo, J. Y., Land, W. H., & Morrison, C. T. (2000). Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of Spie the International Society for Optical Engineering, 3979.
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Lo, J. Y., Land, W. H., & Morrison, C. T. (2000). Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of Spie the International Society for Optical Engineering, 3979, I/-.
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Tourassi, G. D., Floyd, C. E., & Lo, J. Y. (2000). Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation. Proceedings of Spie the International Society for Optical Engineering, 3979.
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Gavrielides, M. A., Lo, J. Y., Vargas-Voracek, R., & Floyd, C. E. (2000). Segmentation of suspicious clustered microcalcifications in mammograms. Medical Physics, 27(1), 13–22. https://doi.org/10.1118/1.598852Full Text
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Land, W. H., Jr, M. T., & Lo, J. Y. (2000). Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Proc. Spie Int. Soc. Opt. Eng. (Usa), 3979, 77–85.
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Land, W. H., Masters, T., Morrison, C. T., & Lo, J. Y. (1999). Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Intelligent Engineering Systems Through Artificial Neural Networks, 9, 803–808.
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Munley, M. T., Lo, J. Y., Sibley, G. S., Bentel, G. C., Anscher, M. S., & Marks, L. B. (1999). A neural network to predict symptomatic lung injury. Phys Med Biol, 44(9), 2241–2249. https://doi.org/10.1088/0031-9155/44/9/311Full Text Link to Item
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Floyd, C. E., Lo, J. Y., & Baker, J. A. (1999). Prediction of breast biopsy outcomes from mammographic findings. Computer Aided Diagnosis in Medical Imaging, 1182, 193–200.Link to Item
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Floyd, C. E., Lo, J. Y., & Tourassi, G. D. (1999). Case-based reasoning as a computer aid to diagnosis. Proceedings of Spie the International Society for Optical Engineering, 3661(I), 486–489.
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Lo, J. Y., & Floyd, C. E. (1999). Application of artificial neural networks for diagnosis of breast cancer. Proceedings of the 1999 Congress on Evolutionary Computation, Cec 1999, 3, 1755–1759. https://doi.org/10.1109/CEC.1999.785486Full Text
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Lo, J. Y., & Floyd, C. E. (1999). Computer-aided diagnosis of breast cancer. Computer Aided Diagnosis in Medical Imaging, 1182, 221–225.Link to Item
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Lo, J. Y., Baker, J. A., Kornguth, P. J., & Floyd, C. E. (1999). Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks. Acad Radiol, 6(1), 10–15. https://doi.org/10.1016/s1076-6332(99)80056-7Full Text Link to Item
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Lo, J. Y., Land, W. H., & Morrison, C. T. (1999). Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis. Proceedings of the International Joint Conference on Neural Networks, 5, 3712–3716.
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Tourassi, G. D., Floyd, C. E., & Lo, J. Y. (1999). Constraint Satisfaction Neural Network for medical diagnosis. Proceedings of the International Joint Conference on Neural Networks, 5, 3632–3635.
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Freed, K. S., Lo, J. Y., Baker, J. A., Floyd, C. E., Low, V. H., Seabourn, J. T., & Nelson, R. C. (1998). Predictive model for the diagnosis of intraabdominal abscess. Acad Radiol, 5(7), 473–479. https://doi.org/10.1016/s1076-6332(98)80187-6Full Text Link to Item
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Sakamoto, H., Lo, J. Y. D., & Nishida, T. (1998). QoS middleware for Internet multimedia streaming. Nec Tech. J. (Japan), 51(8), 35–40.
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Lo, J. Y., & Floyd, C. E. (1997). Self-organizing maps for analyzing mammographic findings. Ieee International Conference on Neural Networks Conference Proceedings, 4, 2472–2474. https://doi.org/10.1109/ICNN.1997.614546Full Text
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Lo, J. Y., Baker, J. A., Kornguth, P. J., Iglehart, J. D., & Floyd, C. E. (1997). Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology, 203(1), 159–163. https://doi.org/10.1148/radiology.203.1.9122385Full Text Link to Item
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Lo, J. Y., Kim, J., Baker, J. A., & Floyd, C. E. (1996). Computer-aided diagnosis of mammography using an artificial neural network: Predicting the invasiveness of breast cancers from image features. Proceedings of Spie the International Society for Optical Engineering, 2710, 725–732. https://doi.org/10.1117/12.237977Full Text
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Floyd, C. E., Patz, E. F., Lo, J. Y., Vittitoe, N. F., & Stambaugh, L. E. (1996). Diffuse nodular lung disease on chest radiographs: a pilot study of characterization by fractal dimension. Ajr Am J Roentgenol, 167(5), 1185–1187. https://doi.org/10.2214/ajr.167.5.8911177Full Text Link to Item
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Jr, F. C. E., Lo, J. Y., Tourassi, G. D., Baker, J. A., Vitittoe, N. F., & Vargas-Vorack, R. (1996). Computer aided diagnosis in thoracic and mammographic radiology. Med. Imaging Technol. (Japan), 14(6), 629–634.
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Baker, J. A., Kornguth, P. J., Lo, J. Y., & Floyd, C. E. (1996). Artificial neural network: improving the quality of breast biopsy recommendations. Radiology, 198(1), 131–135. https://doi.org/10.1148/radiology.198.1.8539365Full Text Link to Item
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Mun, S. K., Freedman, M. T., Wu, Y. C., Lo, B. S., Floyd, C. E., Lo, J. Y., … Kim, Y. (1995). Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography. Proceedings of Spie the International Society for Optical Engineering, 2431, 442–446.
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Lo, J. Y., Baker, J. A., Kornguth, P. J., & Floyd, C. E. (1995). Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. Acad Radiol, 2(10), 841–850. https://doi.org/10.1016/s1076-6332(05)80057-1Full Text Link to Item
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Baker, J. A., Kornguth, P. J., Lo, J. Y., Williford, M. E., & Floyd, C. E. (1995). Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology, 196(3), 817–822. https://doi.org/10.1148/radiology.196.3.7644649Full Text Link to Item
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Lo, J. Y., Grisson, A. T., Floyd, C. E., & Kornguth, P. J. (1995). Computer-aided diagnosis of mammograms using an artificial neural network: Merging of standardized input features from the ACR lexicon. Proceedings of Spie the International Society for Optical Engineering, 2434, 571–578. https://doi.org/10.1117/12.208729Full Text
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Floyd, C. E., Lo, J. Y., Yun, A. J., Sullivan, D. C., & Kornguth, P. J. (1994). Prediction of breast cancer malignancy using an artificial neural network. Cancer, 74(11), 2944–2948. https://doi.org/10.1002/1097-0142(19941201)74:11<2944::aid-cncr2820741109>3.0.co;2-fFull Text Link to Item
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Floyd, C. E., Baydush, A. H., Lo, J. Y., Bowsher, J. E., & Ravin, C. E. (1994). Bayesian restoration of chest radiographs. Scatter compensation with improved signal-to-noise ratio. Invest Radiol, 29(10), 904–910. https://doi.org/10.1097/00004424-199410000-00007Full Text Link to Item
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Lo, J. Y., Baydush, A. H., & Floyd, C. E. (1994). Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network. Proceedings of Spie the International Society for Optical Engineering, 2167, 601–611. https://doi.org/10.1117/12.175095Full Text
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Lo, J. Y., Floyd, C. E., Baker, J. A., & Ravin, C. E. (1994). Scatter compensation in digital chest radiography using the posterior beam stop technique. Med Phys, 21(3), 435–443. https://doi.org/10.1118/1.597388Full Text Link to Item
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Baker, J. A., Floyd, C. E., Lo, J. Y., & Ravin, C. E. (1993). Observer evaluation of scatter subtraction for digital portable chest radiographs. Invest Radiol, 28(8), 667–670. https://doi.org/10.1097/00004424-199308000-00001Full Text Link to Item
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Jordan, L. K., Floyd, C. E., Lo, J. Y., & Ravin, C. E. (1993). Measurement of scatter fractions in erect posteroanterior and lateral chest radiography. Radiology, 188(1), 215–218. https://doi.org/10.1148/radiology.188.1.8511301Full Text Link to Item
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Floyd, C. E., Baydush, A. H., Lo, J. Y., Bowsher, J. E., & Ravin, C. E. (1993). Scatter compensation for digital chest radiography using maximum likelihood expectation maximization. Invest Radiol, 28(5), 427–433. https://doi.org/10.1097/00004424-199305000-00009Full Text Link to Item
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Lo, J. Y., Floyd, C. E., Baker, J. A., & Ravin, C. E. (1993). An artificial neural network for estimating scatter exposures in portable chest radiography. Med Phys, 20(4), 965–973. https://doi.org/10.1118/1.596978Full Text Link to Item
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BAYDUSH, A. H., FLOYD, C. E., LO, J. Y., BOWSHER, J. E., & RAVIN, C. E. (1992). SCATTER REDUCTION IN PORTABLE DIGITAL CHEST RADIOGRAPHY WITH BAYESIAN IMAGE ESTIMATION. Radiology, 185, 305–305.Link to Item
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LO, J. Y., FLOYD, C. E., BOWSHER, J. E., & RAVIN, C. E. (1992). SPATIALLY VARYING SCATTER ESTIMATION IN PORTABLE CHEST RADIOGRAPHY WITH AN ARTIFICIAL NEURAL NETWORK. Radiology, 185, 300–300.Link to Item
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Floyd, C. E., Baker, J. A., Lo, J. Y., & Ravin, C. E. (1992). Measurement of scatter fractions in clinical bedside radiography. Radiology, 183(3), 857–861. https://doi.org/10.1148/radiology.183.3.1584947Full Text Link to Item
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Floyd, C. E., Baker, J. A., Lo, J. Y., & Ravin, C. E. (1992). Posterior beam-stop method for scatter fraction measurement in digital radiography. Invest Radiol, 27(2), 119–123. https://doi.org/10.1097/00004424-199202000-00004Full Text Link to Item
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Floyd, C. E., Lo, J. Y., Chotas, H. G., & Ravin, C. E. (1991). Quantitative scatter measurement in digital radiography using a photostimulable phosphor imaging system. Med Phys, 18(3), 408–413. https://doi.org/10.1118/1.596687Full Text Link to Item
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Chotas, H. G., Floyd, C. E., Dobbins, J. T., Lo, J. Y., & Ravin, C. E. (1990). Scatter fractions in AMBER imaging. Radiology, 177(3), 879–880. https://doi.org/10.1148/radiology.177.3.2244003Full Text Link to Item
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Book Sections
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Robinson, M. D., Chiu, S. J., Toth, C. A., Izatt, J. A., Lo, J. Y., & Farsiu, S. (2017). New applications of super-resolution in medical imaging. In Super-Resolution Imaging (pp. 383–412). https://doi.org/10.1201/9781439819319Full Text
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Robinson, M. D., Farsiu, S., Lo, J. Y., & Toth, C. A. (2008). Efficient restoration and enhancement of super-resolved X-ray images (pp. 629–632). https://doi.org/10.1109/ICIP.2008.4711833Full Text
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Jesneck, J. L., Mukherjee, S., Nolte, L. W., Lokshin, A. E., Marks, J. R., & Lo, J. (2007). Decision fusion of circulating markers for breast cancer detection in premenopausal women (pp. 1434–1438). https://doi.org/10.1109/BIBE.2007.4375762Full Text
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Land, W. H., McKee, D. W., Anderson, F. R., Masters, T., Lo, J. Y., Embrechts, M., & Heine, J. (2006). Using computational intelligence for computer-aided diagnosis of screen-film mammograms. In Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer (pp. 315–375). https://doi.org/10.1117/3.651880.ch10Full Text
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Lo, J. Y., Bilska-Wolak, A. O., Baker, J. A., Tourassi, G. D., Floyd, C. E., & Markey, M. K. (2006). Computer-aided diagnosis in breast imaging: Where do we go after detection? In Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer (pp. 871–900). https://doi.org/10.1117/3.651880.ch27Full Text
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Other Articles
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Robinson, M. D., Farsiu, S., Lo, J. Y., Milanfar, P., & Toth, C. A. (2007). Efficient registration of aliased x-ray images. Conference Record Asilomar Conference on Signals, Systems and Computers. https://doi.org/10.1109/ACSSC.2007.4487198Full Text
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Conference Papers
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Barnett, A. J., Sharma, V., Gajjar, N., Fang, J., Schwartz, F. R., Chen, C., … Rudin, C. (2022). Interpretable Deep Learning Models for Better Clinician-AI Communication in Clinical Mammography. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 12035). https://doi.org/10.1117/12.2612372Full Text
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Dahal, L., Tushar, F. I., Abadi, E., Fricks, R. B., Mazurowski, M., Segars, W. P., … Lo, J. Y. (2022). Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 12031). https://doi.org/10.1117/12.2612975Full Text
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Tushar, F. I., Abadi, E., Sotoudeh-Paima, S., Fricks, R. B., Mazurowski, M. A., Segars, W. P., … Lo, J. Y. (2022). Virtual vs. reality: External validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 12033). https://doi.org/10.1117/12.2613010Full Text
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Tushar, F. I., Danniballe, V. M., Rubin, G. D., Samei, E., & Lo, J. Y. (2022). Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 12033). https://doi.org/10.1117/12.2612700Full Text
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Tushar, F. I., Nujaim, H., Fu, W., Abadi, E., Mazurowski, M. A., Segars, W. P., … Lo, J. Y. (2022). Quality or quantity: toward a unified approach for multi-organ segmentation in body CT. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 12031). https://doi.org/10.1117/12.2613101Full Text
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Fu, W., Segars, P. W., Sharma, S., Lo, J. Y., & Samei, E. (2021). IPhantom: An automated framework in generating personalized computational phantoms for organ-based radiation dosimetry. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11595). https://doi.org/10.1117/12.2582238Full Text
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Ren, Y., Lu, J., Liang, Z., Grimm, L. J., Kim, C., Taylor-Cho, M., … Lo, J. Y. (2021). Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12905 LNCS, pp. 345–354). https://doi.org/10.1007/978-3-030-87240-3_33Full Text
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Faryna, K., Tushar, F. I., D’Anniballe, V. M., Hou, R., Rubin, G. D., & Lo, J. Y. (2020). Attention-guided classification of abnormalities in semi-structured computed tomography reports. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11314). https://doi.org/10.1117/12.2551370Full Text
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Ikejimba, L. C., Salad, J., Graff, C. G., Goodsitt, M., Chan, H. P., Zhao, W., … Glick, S. J. (2020). Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom. In Proceedings of Spie the International Society for Optical Engineering (Vol. 11513). https://doi.org/10.1117/12.2564357Full Text
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Saha, A., Tushar, F. I., Faryna, K., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., … Lo, J. Y. (2020). Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11314). https://doi.org/10.1117/12.2550857Full Text
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Hou, R., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., Maley, C. C., … Lo, J. Y. (2020). A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11314). https://doi.org/10.1117/12.2549669Full Text
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Pegues, H., Tong, H., Wiley, B. J., Samei, E., & Lo, J. Y. (2020). CT phantom with 3D anthropomorphic, contrast-enhanced texture. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11312). https://doi.org/10.1117/12.2549734Full Text
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Peng, Y., Hou, R., Ren, Y., Grimm, L. J., Marks, J. R., Hwang, E. S., & Lo, J. Y. (2020). Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11314). https://doi.org/10.1117/12.2551263Full Text
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Samei, E., Abadi, E., Kapadia, A., Lo, J., Mazurowski, M., & Segars, P. (2020). Virtual imaging trials: An emerging experimental paradigm in imaging research and practice. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11312). https://doi.org/10.1117/12.2549818Full Text
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Vancoillie, L., Cockmartin, L., Marshall, N. W., Lo, J. Y., & Bosmans, H. (2020). Evaluation of possible phantoms for assessment of image quality in synthetic mammograms. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 11312). https://doi.org/10.1117/12.2548465Full Text
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Benitez, M., Tian, J., Kelly, M., Selvakumaran, V., Phelan, M., Mazurowski, M., … Henao, R. (2019). Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2512886Full Text
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Geng, Y., Ren, Y., Hou, R., Han, S., Rubin, G. D., & Lo, J. Y. (2019). 2.5D CNN model for detecting lung disease using weak supervision. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2513631Full Text
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Han, S., Tian, J., Kelly, M., Selvakumaran, V., Henao, R., Rubin, G. D., & Lo, J. Y. (2019). Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2513577Full Text
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Liu, Y., Fu, W., Selvakumaran, V., Phelan, M., Segars, W. P., Samei, E., … Henao, R. (2019). Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10954). https://doi.org/10.1117/12.2512887Full Text
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Pegues, H., Knudsen, J., Tong, H., Gehm, M. E., Wiley, B. J., Samei, E., & Lo, J. Y. (2019). Using inkjet 3D printing to create contrast-enhanced textured physical phantoms for CT. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10948). https://doi.org/10.1117/12.2512890Full Text
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Tang, R., Tushar, F. I., Han, S., Hou, R., Rubin, G. D., & Lo, J. Y. (2019). Classification of chest CT using case-level weak supervision. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2513576Full Text
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Tong, H., Pegues, H., Yang, F., Samei, E., Lo, J. Y., & Wiley, B. J. (2019). Controlling the position-dependent contrast of 3D printed physical phantoms with a single material. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10948). https://doi.org/10.1117/12.2513469Full Text
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Fu, W., Sharma, S., Smith, T., Hou, R., Abadi, E., Selvakumaran, V., … others, . (2019). Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology. In Medical Imaging 2019: Physics of Medical Imaging (Vol. 10948, pp. 1094829–1094829). International Society for Optics and Photonics.
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Kong, D., Ren, Y., Hou, R., Grimm, L. J., Marks, J. R., & Lo, J. Y. (2019). Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2513125Full Text
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Ren, Y., Hou, R., Kong, D., Geng, Y., Grimm, L. J., Marks, J. R., & Lo, J. Y. (2019). Multiview mammographic mass detection based on a single shot detection system. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10950). https://doi.org/10.1117/12.2513136Full Text
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Ren, Y., Zhu, Z., Li, Y., Kong, D., Hou, R., Grimm, L. J., … Lo, J. Y. (2019). Mask Embedding for Realistic High-Resolution Medical Image Synthesis. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11769 LNCS, pp. 422–430). https://doi.org/10.1007/978-3-030-32226-7_47Full Text
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Lo, J. Y., Grimm, L. J., Mazurowski, M. A., Baker, J. A., Marks, J. R., King, L. M., … Shi, B. (2018). Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features. In Cancer Research (Vol. 78). San Antonio, TX: AMER ASSOC CANCER RESEARCH.Link to Item
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Ikejimba, L. C., Salad, J., Kemp, K., Graff, C. G., Ghammraoui, B., Lo, J. Y., & Glick, S. J. (2018). Method for task-based evaluation of clinical FFDM and DBT systems using an anthropomorphic breast phantom. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10718). https://doi.org/10.1117/12.2318523Full Text
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Ikejimba, L. C., Yan, T., Kemp, K., Salad, J., Graff, C. G., Ghammraoui, B., … Glick, S. J. (2018). Methodology for the objective assessment of lesion detection performance with breast tomosynthesis and digital mammography using a physical anthropomorphic phantom. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10573). https://doi.org/10.1117/12.2294562Full Text
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Rajagopal, J., Sturgeon, G. M., Chen, X. C., Sauer, T. J., Ren, Y., Segars, W. P., & Lo, J. Y. (2018). Evaluation of statistical breast phantoms with higher resolution. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10573). https://doi.org/10.1117/12.2294049Full Text
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Rossman, A., Catenacci, M., Li, A. M., Sauer, T. J., Solomon, J., Gehm, M. E., … Lo, J. Y. (2018). 3D printed anthropomorphic physical phantom for mammography and DBT with high contrast custom materials, lesions, and uniform chest wall region. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10573). https://doi.org/10.1117/12.2294519Full Text
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Wei, Q., Ren, Y., Hou, R., Shi, B., Lo, J. Y., & Carin, L. (2018). Anomaly detection for medical images based on a one-class classification. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10575). https://doi.org/10.1117/12.2293408Full Text
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Hou, R., Shi, B., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., … Lo, J. Y. (2018). Improving classification with forced labeling of other related classes: Application to prediction of upstaged ductal carcinoma in situ using mammographic features. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10575). https://doi.org/10.1117/12.2293809Full Text
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Shi, B., Hou, R., Mazurowski, M. A., Grimm, L. J., Ren, Y., Marks, J. R., … Lo, J. Y. (2018). Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10575). https://doi.org/10.1117/12.2293594Full Text
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Chen, X., Gong, X., Graff, C. G., Santana, M., Sturgeon, G. M., Sauer, T. J., … Lo, J. Y. (2017). High-resolution, anthropomorphic, computational breast phantom: Fusion of rule-based structures with patient-based anatomy. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10132). https://doi.org/10.1117/12.2255913Full Text
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Ikejimba, L. C., Graff, C. G., Rosenthal, S., Badal, A., Ghammraoui, B., Lo, J. Y., & Glick, S. J. (2017). A physical breast phantom for 2D and 3D x-ray imaging made through inkjet printing. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10132). https://doi.org/10.1117/12.2255016Full Text
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Reinhold, J., Wen, G., Lo, J. Y., & Markey, M. K. (2017). Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: A model observer study with anthropomorphic computational breast phantoms. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10136). https://doi.org/10.1117/12.2255909Full Text
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Sauer, T. J., Graff, C. G., Zeng, R., Santana, M., Sturgeon, G. M., Bosmans, H., … Lo, J. Y. (2017). Detectability of artificial lesions in anthropomorphic virtual breast phantoms of variable glandular fraction. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10132). https://doi.org/10.1117/12.2255896Full Text
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Zhao, A., Santana, M., Samei, E., & Lo, J. (2017). Comparison of effects of dose on image quality in digital breast tomosynthesis across multiple vendors. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10132). https://doi.org/10.1117/12.2255570Full Text
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Zhao, C., Solomon, J., Sturgeon, G. M., Gehm, M. E., Catenacci, M., Wiley, B. J., … Lo, J. Y. (2017). Third generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and uniform chest wall QC region. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10132). https://doi.org/10.1117/12.2256091Full Text
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Shi, B., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., Maley, C. C., … Lo, J. Y. (2017). Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10134). https://doi.org/10.1117/12.2255731Full Text
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Shi, B., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., Maley, C. C., … Lo, J. Y. (2017). Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features? In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 10134). https://doi.org/10.1117/12.2255847Full Text
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Ikejimba, L., Glick, S. J., Samei, E., & Lo, J. Y. (2016). Comparison of model and human observer performance in FFDM, DBT, and synthetic mammography. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9783). https://doi.org/10.1117/12.2216858Full Text
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Makeev, A., Ikejimba, L., Lo, J. Y., & Glick, S. J. (2016). Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9783). https://doi.org/10.1117/12.2217438Full Text
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Sikaria, D., Musinsky, S., Sturgeon, G. M., Solomon, J., Diao, A., Gehm, M. E., … Lo, J. Y. (2016). Second generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and inkjet printing of iodinated lesion inserts. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9783). https://doi.org/10.1117/12.2217667Full Text
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Solomon, J., Ba, A., Diao, A., Lo, J., Bier, E., Bochud, F., … Samei, E. (2016). Design, fabrication, and implementation of voxel-based 3D printed textured phantoms for task-based image quality assessment in CT. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9783). https://doi.org/10.1117/12.2217463Full Text
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Sturgeon, G. M., Tward, D. J., Ketcha, M., Ratnanather, J. T., Miller, M. I., Park, S., … Lo, J. Y. (2016). Eigenbreasts for statistical breast phantoms. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9783). https://doi.org/10.1117/12.2216398Full Text
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Wang, M., Zhang, J., Grimm, L. J., Ghate, S. V., Walsh, R., Johnson, K. S., … Mazurowski, M. A. (2016). Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9787). https://doi.org/10.1117/12.2201061Full Text
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Ikejimba, L., Chen, Y., Oberhofer, N., Kiarashi, N., Lo, J. Y., & Samei, E. (2015). A quantitative metrology for performance characterization of breast tomosynthesis systems based on an anthropomorphic phantom. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9412). https://doi.org/10.1117/12.2082594Full Text
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Kiarashi, N., Nolte, L. W., Lo, J. Y., Segars, W. P., Ghate, S. V., & Samei, E. (2015). The impact of breast structure on lesion detection in breast tomosynthesis. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9412). https://doi.org/10.1117/12.2082473Full Text
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Grimm, L. J., Zhang, J., Johnson, K. S., Lo, J. Y., & Mazurowski, M. A. (2015). Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality? In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9416). https://doi.org/10.1117/12.2082810Full Text
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Ikejimba, L. C., Kiarashi, N., Ghate, S. V., Samei, E., & Lo, J. Y. (2014). Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis. In Med Phys (Vol. 41, p. 061908). United States. https://doi.org/10.1118/1.4873317Full Text Link to Item
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Mahadevan, R., Ikejimba, L. C., Lin, Y., Samei, E., & Lo, J. Y. (2014). A task-based comparison of two reconstruction algorithms for digital breast tomosynthesis. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9033). https://doi.org/10.1117/12.2043829Full Text
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Mazurowski, M. A., Zhang, J., Lo, J. Y., Kuzmiak, C. M., Ghate, S. V., & Yoon, S. (2014). Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9037). https://doi.org/10.1117/12.2044404Full Text
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Nolte, A., Kiarashi, N., Samei, E., Segars, W. P., & Lo, J. Y. (2014). A second generation of physical anthropomorphic 3D breast phantoms based on human subject data. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9033). https://doi.org/10.1117/12.2043703Full Text
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Segars, W. P., Veress, A. I., Wells, J. R., Sturgeon, G. M., Kiarashi, N., Lo, J. Y., … Dobbins, J. T. (2014). Population of 100 realistic, patient-based computerized breast phantoms for multi-modality imaging research. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 9033). https://doi.org/10.1117/12.2043868Full Text
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Kiarashi, N., Sturgeon, G. M., Nolte, L. W., Lo, J. Y., III, J. T. D., Segars, W. P., & Samei, E. (2013). Development of matched virtual and physical breast phantoms based on patient data. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 8668). https://doi.org/10.1117/12.2008406Full Text
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Chanyavanich, V., Lo, J., & Das, S. (2012). SU-E-T-572: A Plan Quality Metric for Evaluating Knowledge-Based Treatment Plans. In Med Phys (Vol. 39, p. 3837). United States. https://doi.org/10.1118/1.4735661Full Text Link to Item
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Dick, D., Das, S., & Lo, J. (2012). WE-G-BRCD-06: Knowledge-Based Intensity Modulated Radiotherapy (IMRT) Treatment Planning for Prostate Cancer. In Med Phys (Vol. 39, pp. 3965–3966). United States. https://doi.org/10.1118/1.4736183Full Text Link to Item
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Kiarashi, N., Lin, Y., Segars, W. P., Ghate, S. V., Ikejimba, L., Chen, B., … Samei, E. (2012). Development of a dynamic 4D anthropomorphic breast phantom for contrast-based breast imaging. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 8313). https://doi.org/10.1117/12.913332Full Text
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Lin, Y., Ghate, S., Lo, J., & Samei, E. (2012). 3D biopsy for tomosynthesis: Simulation of prior information based reconstruction for dose and artifact reduction. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 8313). https://doi.org/10.1117/12.912346Full Text
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Ikejimba, L., Kiarashi, N., Lin, Y., Chen, B., Ghate, S. V., Zerhouni, M., … Lo, J. Y. (2012). Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis. In N. J. Pelc, R. M. Nishikawa, & B. R. Whiting (Eds.), Spie Proceedings. SPIE. https://doi.org/10.1117/12.913377Full Text
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Shafer, C. M., Seewaldt, V. L., & Lo, J. Y. (2011). Segmentation of adipose and glandular tissue for breast tomosynthesis imaging using a 3D hidden-Markov model trained on breast MRIs. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 7961). https://doi.org/10.1117/12.878137Full Text
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Chanyavanich, V., Das, S., & lo, J. (2011). SU‐E‐T‐851: An Inter‐Institutional Comparison of Knowledge‐Based IMRT Treatment Planning for Prostate Cancer. In Medical Physics (Vol. 38, p. 3687). https://doi.org/10.1118/1.3612815Full Text
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Mazurowski, M. A., Lo, J. Y., & Tourassi, G. D. (2010). User modeling for improved computer-aided training in radiology: Initial experience. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 7627). https://doi.org/10.1117/12.843863Full Text
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Chanyavanich, V., Freeman, M., Das, S., & lo, J. (2010). SU‐GG‐T‐134: Knowledge‐Based IMRT Treatment Planning for Prostate Cancer. In Medical Physics (Vol. 37, p. 3215). https://doi.org/10.1118/1.3468524Full Text
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Freeman, M. S., Chanyavanich, V., Das, S. K., & lo, J. Y. (2010). SU‐GG‐T‐131: A Linear Metric of Knowledge‐Based IMRT Treatment Plan Quality for the Prostate. In Medical Physics (Vol. 37, p. 3214). https://doi.org/10.1118/1.3468521Full Text
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Mehtaji, D., Shafer, C., Chen, B., & lo, J. (2010). SU‐GG‐I‐154: Evaluation of Quantitative Potential of Breast Tomosynthesis Using a Voxelized Anthropomorphic Breast Phantom. In Medical Physics (Vol. 37, p. 3137). https://doi.org/10.1118/1.3468190Full Text
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lo, J. Y., Baydush, A. H., Karim, E., & Mendonca, S. J. (2010). WE‐A‐201B‐04: Reducing Dose in Breast Tomosynthesis Using Bayesian Image Estimation. In Medical Physics (Vol. 37, p. 3413). https://doi.org/10.1118/1.3469336Full Text
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Chawla, A. S., Samei, E., & Lo, J. Y. (2009). Optimized lesion detection in breast tomosynthesis. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 7258). https://doi.org/10.1117/12.813964Full Text
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Li, C. M., Segars, W. P., Lo, J. Y., Veress, A. I., Boone, J. M., & Dobbins, J. T. (2009). Computerized 3D breast phantom with enhanced High-Resolution detail. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 7258). https://doi.org/10.1117/12.813529Full Text
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Shafer, C., lo, J., & Samei, E. (2009). MO‐FF‐A4‐01: Evaluation of Background Trend Correction Technique in Breast Tomosynthesis Quantitation. In Medical Physics (Vol. 36, p. 2713). https://doi.org/10.1118/1.3182295Full Text
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Xia, J. Q., & Lo, J. Y. (2008). Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal. In 8th Ieee International Conference on Bioinformatics and Bioengineering, Bibe 2008. https://doi.org/10.1109/BIBE.2008.4696788Full Text
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Chawla, A. S., Samei, E., Lo, J. Y., & Mertelmeier, T. (2008). Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 635–642). https://doi.org/10.1007/978-3-540-70538-3_88Full Text
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Shafer, C. M., Samei, E., Mertelmeier, T., Saunders, R. S., Zerhouni, M., & Lo, J. Y. (2008). Assessment of low energies and slice depth in the quantification of breast tomosynthesis. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 530–536). https://doi.org/10.1007/978-3-540-70538-3_74Full Text
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Singh, S., Tourassi, G. D., & Lo, J. Y. (2008). Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 286–291). https://doi.org/10.1007/978-3-540-70538-3_40Full Text
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Tourassi, G. D., Sharma, A. C., Singh, S., Saunders, R. S., Lo, J. Y., Samei, E., & Harrawood, B. P. (2008). Knowledge transfer across breast cancer screening modalities: A pilot study using an information-theoretic CADe system for mass detection. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 292–298). https://doi.org/10.1007/978-3-540-70538-3_41Full Text
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Chawla, A. S., Samei, E., Saunders, R. S., Lo, J. Y., & Singh, S. (2008). Optimized acquisition scheme for multi-projection correlation imaging of breast cancer. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6915). https://doi.org/10.1117/12.773174Full Text
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Singh, S., Tourassi, G. D., Chawla, A. S., Saunders, R. S., Samei, E., & Lo, J. Y. (2008). Computer aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6915). https://doi.org/10.1117/12.772978Full Text
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Li, C. M., Segars, W. P., Lo, J. Y., Veress, A. I., Boone, J. M., & Dobbins, J. T. (2008). Three-dimensional computer generated breast phantom based on empirical data. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6913). https://doi.org/10.1117/12.772185Full Text
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Shafer, C. M., Samei, E., Saunders, R. S., Zerhouni, M., & Lo, J. Y. (2008). Toward quantification of breast tomosynthesis imaging. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6913). https://doi.org/10.1117/12.772753Full Text
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Robinson, M. D., Farsiu, S., Lo, J. Y., & Toth, C. A. (2008). Efficient Restoration and Enhancement of Super-resolved X-ray Images. In 2008 Ieee International Conference on Image Processing, Proceedings (pp. 633-+). San Diego, CA: IEEE.Link to Item
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lo, J. Y. (2008). TU‐D‐342‐02: What Every Medical Physicist Should Know About Breast Tomosynthesis. In Medical Physics (Vol. 35, p. 2908). https://doi.org/10.1118/1.2962602Full Text
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Jerebko, A., Quan, Y., Merlet, N., Ratner, E., Singh, S., Lo, J. Y., & Krishnan, A. (2007). Feasibility study of breast tomosynthesis CAD system. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6514). https://doi.org/10.1117/12.712729Full Text
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Saunders, R. S., Samei, E., Majdi-Nasab, N., & Lo, J. Y. (2007). Initial human subject results for breast Bi-plane correlation imaging technique. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6514). https://doi.org/10.1117/12.713722Full Text
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Chen, Y., Lo, J. Y., Ranger, N. T., Samei, E., & Dobbins, J. T. (2007). Methodology of NEQ (f) analysis for optimization and comparison of digital breast tomosynthesis acquisition techniques and reconstruction algorithms. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6510). https://doi.org/10.1117/12.713737Full Text
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Johnson, J. P., Lo, J., Mertelmeier, T., Nafziger, J. S., Timberg, P., & Samei, E. (2007). Visual image quality metrics for optimization of breast tomosynthesis acquisition technique. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6515). https://doi.org/10.1117/12.712343Full Text
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Xia, J. Q., Tourassi, G. D., Lo, J. Y., & Floyd, C. E. (2007). On the development of a Gaussian noise model for scatter compensation. In Progress in Biomedical Optics and Imaging Proceedings of Spie (Vol. 6510). https://doi.org/10.1117/12.712515Full Text
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Williams, M., Raghunathan, P., Seibert, J. A., Kwan, A., lo, J., Samei, E., … Mawdsley, G. (2007). TU‐B‐M100J‐01: Optimizing Mammography Image Quality and Dose: X‐Ray Spectrum and Exposure Parameter Selection. In Medical Physics (Vol. 34, pp. 2540–2541). https://doi.org/10.1118/1.2761315Full Text
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lo, J. Y., Singh, S., Dobbins, J. T., & Samei, E. (2007). MO‐D‐L100F‐03: New Developments in Digital Breast Tomosynthesis. In Medical Physics (Vol. 34, p. 2518). https://doi.org/10.1118/1.2761222Full Text
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Chen, Y., Lo, J., Baker, J., & Dobbins, J. (2006). SU‐FF‐I‐21: Two‐Dimensional Shift‐And‐Add (SAA) Algorithm for Digital Breast Tomosynthesis Reconstruction. In Medical Physics (Vol. 33, p. 2001). https://doi.org/10.1118/1.2240260Full Text
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Hong, A. S., Baker, J. A., Lo, J. Y., Nicholas, J. L., & Soo, M. S. (2004). Computer-aided classification of breast masses using mammogram, ultrasound, and clinical inputs. In American Journal of Roentgenology (Vol. 182, pp. 33–33). Miami Beach, FL: AMER ROENTGEN RAY SOC.Link to Item
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Land, W. H., Masters, T., Lo, J. Y., McKee, D. W., & Anderson, F. R. (2001). New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data. In Smcia 2001 Proceedings of the 2001 Ieee Mountain Workshop on Soft Computing in Industrial Applications (pp. 47–52). https://doi.org/10.1109/SMCIA.2001.936727Full Text
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Baydush, A. H., Catarious, D. M., Lo, J. Y., Abbey, C. K., & Floyd, C. E. (2001). Computer-aided detection of lung nodules in chest radiographs using sub-region Hotelling observers. In Radiology (Vol. 221, pp. 548–548).Link to Item
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LO, J. Y., BAYDUSH, A. H., BAKER, J. A., KORNGUTH, P. J., & FLOYD, C. E. (1995). COMPUTER-AIDED DIAGNOSIS OF BREAST MASS MALIGNANCY WITH AUTOMATED FEATURE-EXTRACTION AND ARTIFICIAL NEURAL NETWORKS. In Radiology (Vol. 197, pp. 425–425).Link to Item
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FLOYD, C. E., YUN, A. J., LO, J. Y., TOURASSI, G., SULLIVAN, D. C., & KORNGUTH, P. J. (1994). PREDICTION OF BREAST-CANCER MALIGNANCY FOR DIFFICULT CASES USING AN ARTIFICIAL NEURAL-NETWORK. In World Congress on Neural Networks San Diego 1994 International Neural Network Society Annual Meeting, Vol 1 (pp. A127–A132). SAN DIEGO, CA: LAWRENCE ERLBAUM ASSOC PUBL.Link to Item
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Floyd, C. E., Bowsher, J. E., Munley, M. T., Tourassi, G. D., Garg, S., Baydush, A. H., … Coleman, R. E. (n.d.). Artificial neural networks for SPECT image reconstruction with optimized weighted backprojection (Accepted). In Conference Record of the 1991 Ieee Nuclear Science Symposium and Medical Imaging Conference. IEEE. https://doi.org/10.1109/nssmic.1991.259306Full Text
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