Joseph Yuan-Chieh Lo | Scholars@Duke

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
joseph.lo@duke.edu (919) 684-7763
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Background
Education, Training, & Certifications
- Research Associate, Radiology, Duke University 1993 - 1995
- Ph.D., Duke University 1993
- B.S.E.E., Duke University 1988
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
Leadership & Clinical Positions at Duke
- Vice Chair for Research of Radiology
  Associate Director, Medical Physics Graduate Program
Recognition
In the News
- JAN 20, 2022 Pratt School of Engineering
  
  The First AI Breast Cancer Sleuth That Shows Its Work
Expertise
Subject Headings
Global Scholarship
- Expertise
  - United States of America (Country)
Research
Selected Grants
- 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
Selected Publications
- Academic Articles
  - 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.2021.3126281
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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.210407
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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.15494
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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.2021.3130397
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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.210026
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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-x
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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/bbab221
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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.2021.19100
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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 J Biomed Health Inform, 25(8), 3061–3072. https://doi.org/10.1109/JBHI.2021.3063080
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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/life11080747
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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.20.23679
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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.
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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.14568
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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.
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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.2020.101857
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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.2020.101857
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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.
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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.2019.12.009
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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.2020.2991120
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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.
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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.7.4.042805
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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.2019.2940195
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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.2020.0265
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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.13629
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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.
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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.2019182599
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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.6.2.021604
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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.6.2.021601
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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.18.20294
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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/10.1117/12.2512829
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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.2017.11.036
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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.5.1.015501
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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.12579
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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.2017.03.013
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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.12062
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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/10.1118/1.4962475
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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.2016.01.053
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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.3.3.035504
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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/10.1118/1.4945275
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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/10.1118/1.4943373
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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.2015.09.025
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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/10.1118/1.4937597
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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/10.1118/1.4923174
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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.2015.02.025
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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/10.1118/1.4919771
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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/10.1118/1.4892600
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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/10.1118/1.4892173
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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.2014.2312733
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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.2013.03.015
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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/10.1118/1.4807642
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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/10.1117/12.2008398
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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_85
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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.2011.06.011
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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.2011.04.008
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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.2011.5872317
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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/10.1118/1.3574874
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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/10.1118/1.3562901
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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.10081308
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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.2010.2050107
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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/10.1118/1.3276732
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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/10.1118/1.3285038
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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/10.1118/1.3231814
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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.2521081278
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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/10.1186/1471-2407-9-164
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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.
  - 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.
  - 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)80057-1
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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.196.3.7644649
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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/10.1117/12.208729
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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-f
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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/10.1097/00004424-199410000-00007
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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/10.1117/12.175095
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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/10.1118/1.597388
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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/10.1097/00004424-199308000-00001
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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.188.1.8511301
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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/10.1118/1.596978
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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/10.1097/00004424-199305000-00009
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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.
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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.
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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.183.3.1584947
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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/10.1097/00004424-199202000-00004
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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/10.1118/1.596687
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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.177.3.2244003
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- Book Sections
  - 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/10.1201/9781439819319
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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.2008.4711833
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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.2007.4375762
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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.ch10
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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.ch27
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- Other Articles
  - 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.2007.4487198
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- Conference Papers
  - 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/10.1117/12.2612372
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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/10.1117/12.2612975
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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/10.1117/12.2613101
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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/10.1117/12.2582238
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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_33
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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/10.1117/12.2551370
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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/10.1117/12.2549669
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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/10.1117/12.2564357
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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/10.1117/12.2551263
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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/10.1117/12.2550857
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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/10.1117/12.2549734
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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/10.1117/12.2549818
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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/10.1117/12.2548465
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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/10.1117/12.2512886
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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/10.1117/12.2513631
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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/10.1117/12.2513577
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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/10.1117/12.2513125
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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/10.1117/12.2512887
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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/10.1117/12.2512890
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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/10.1117/12.2513136
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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_47
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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/10.1117/12.2513576
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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/10.1117/12.2513469
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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.
  - 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.
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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/10.1117/12.2293809
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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/10.1117/12.2318523
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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/10.1117/12.2294562
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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/10.1117/12.2294049
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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/10.1117/12.2294519
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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/10.1117/12.2293594
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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/10.1117/12.2293408
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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/10.1117/12.2255913
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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/10.1117/12.2255016
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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/10.1117/12.2255909
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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/10.1117/12.2255896
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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/10.1117/12.2255731
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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/10.1117/12.2255847
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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/10.1117/12.2255570
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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/10.1117/12.2256091
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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/10.1117/12.2216858
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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/10.1117/12.2217438
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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/10.1117/12.2217667
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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/10.1117/12.2217463
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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/10.1117/12.2216398
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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/10.1117/12.2201061
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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/10.1117/12.2082810
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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/10.1117/12.2082594
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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/10.1117/12.2082473
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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/10.1118/1.4873317
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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/10.1117/12.2043829
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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/10.1117/12.2044404
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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/10.1117/12.2043703
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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/10.1117/12.2043868
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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/10.1117/12.2008406
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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/10.1118/1.4735661
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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/10.1118/1.4736183
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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/10.1117/12.913332
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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/10.1117/12.912346
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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 Spie Proceedings. SPIE. https://doi.org/10.1117/12.913377
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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/10.1117/12.878137
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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/10.1118/1.3612815
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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/10.1117/12.843863
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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/10.1118/1.3468524
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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/10.1118/1.3468521
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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/10.1118/1.3468190
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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/10.1118/1.3469336
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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/10.1117/12.813964
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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/10.1117/12.813529
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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/10.1118/1.3182295
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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.2008.4696788
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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_88
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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_74
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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_40
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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_41
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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/10.1117/12.773174
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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/10.1117/12.772978
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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/10.1117/12.772185
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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/10.1117/12.772753
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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.
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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/10.1118/1.2962602
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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/10.1117/12.712729
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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/10.1117/12.713722
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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/10.1117/12.713737
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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/10.1117/12.712343
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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/10.1117/12.712515
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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/10.1118/1.2761315
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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/10.1118/1.2761222
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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/10.1118/1.2240260
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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.
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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).
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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.2001.936727
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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).
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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.
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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.1991.259306
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