Joseph Yuan-Chieh Lo | Scholars@Duke

Joseph Yuan-Chieh Lo
Professor of Radiology

My research focuses on computer vision and machine learning in medical imaging, with a focus on mammography and CT imaging. There are three specific projects:

First, we have a long track record of creating machine learning models to detect and diagnose breast cancer from mammograms. These algorithms are based on computer vision and deep learning, with the long term goal to incorporate the contribution of imaging data with proteomic/genomic markers. Specific projects include predicting which cases of DCIS are likely to contain hidden invasive cancer, thus informing women to take advantage of personalized treatment decisions. This work is funded by NIH, Dept of Defense, Cancer Research UK, and other agencies.

Second, we design virtual breast models that are based on actual patient data and thus contain highly realistic breast anatomy with voxel-level ground truth. We can transform these virtual models into physical form using several forms of 3D printing technology. In work funded by NIH, we are translating this work to produce a new generation of realistic phantoms for CT. Such physical phantoms can be scanned on actual imaging devices, allowing us to assess image quality in new ways that are not only quantitative but also clinically relevant.

Third, we are developing a broad, machine learning platform to segment multiple organs and classify multiple diseases in chest-abdomen-pelvis CT scans. The goal is to provide automated labeling of hospital-scale data sets (potentially hundreds of thousands of studies) to produce sufficient data for deep learning studies. This work includes natural language processing to analyze radiology reports, and deep learning models for the segmentation and classification tasks.

Current Appointments & Affiliations

Professor of Radiology, Radiology, Clinical Science Departments 2018
Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2014
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
Ravin Advanced Imaging Labs, 2424 Erwin Road, Suite 302, Durham, NC 27705
joseph.lo@duke.edu (919) 684-7763
Google Scholar
Home Page

Background
Education, Training, & Certifications
- Research Associate, Radiology, Duke University 1993 - 1995
- Ph.D., Duke University 1993
- B.S.E.E., Duke University 1988
Duke Appointment History
- Professor of Radiology, Radiology, Clinical Science Departments 2014 - 2018
- Professor of Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2014 - 2017
- 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
- Associate Vice Chair for Research of Radiology
  Director, Carl E. Ravin Advanced Imaging Laboratories (RAI Labs)
  Director, Radiology, Radiation Oncology, and Medical Physics (RROMP) Study Program, Duke Univ. School of Medicine
Expertise
Subject Headings
Global Scholarship
- Expertise
  - United States of America (Country)
Research
Selected Grants
- Breast Cancer Detection Consortium awarded by National Institutes of Health 2016 - 2021
- 3D Printing of Anatomically Realistic Phantoms for Optimization of Imaging Algorithms awarded by National Institutes of Health 2018 - 2021
- Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS awarded by Department of Defense 2014 - 2020
- Training in Medical Imaging awarded by National Institutes of Health 2003 - 2020
- Machine learning and collaborative filtering tools for personalized education in digital breast tomosynthesis awarded by National Institutes of Health 2016 - 2020
- Prevent Ductal Carcinoma in Situ Invasive Overtreatment Now - PRECISION 2017 - 2020
- (PQC3) Genomic Diversity and 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 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 1999 - 2002
- Computer Aid for the Decision to Biopsy Breast Lesions awarded by National Institutes of Health 1999 - 2001
External Relationships
- Gammex / Sun Nuclear
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Hou, Rui, et al. “Prediction of Upstaged Ductal Carcinoma in situ Using Forced Labeling and Domain Adaptation.” Ieee Trans Biomed Eng, Sept. 2019. Pubmed, doi:10.1109/TBME.2019.2940195.
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  - Ikejimba, Lynda C., et al. “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, vol. 46, no. 9, Sept. 2019, pp. 3883–92. Pubmed, doi:10.1002/mp.13629.
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  - Grimm, Lars J., et al. “Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease.” Radiology, vol. 292, no. 1, July 2019, pp. 77–83. Pubmed, doi:10.1148/radiol.2019182599.
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  - Georgian-Smith, Dianne, et al. “Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis.” Ajr Am J Roentgenol, Apr. 2019, pp. 1–7. Pubmed, doi:10.2214/AJR.18.20294.
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  - Rossman, Andrea H., et al. “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), vol. 6, no. 2, Apr. 2019, p. 021604. Pubmed, doi:10.1117/1.JMI.6.2.021604.
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  - Samei, E., and J. Lo. “Special Section Guest Editorial: Special Section on 3D Printing in Medical Imaging.” Journal of Medical Imaging, vol. 6, no. 2, Apr. 2019. Scopus, doi:10.1117/1.JMI.6.2.021601.
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  - Shi, Bibo, et al. “Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.” J Am Coll Radiol, vol. 15, no. 3 Pt B, Mar. 2018, pp. 527–34. Pubmed, doi:10.1016/j.jacr.2017.11.036.
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  - Wen, Gezheng, et al. “Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images.” J Med Imaging (Bellingham), vol. 5, no. 1, Jan. 2018, p. 015501. Pubmed, doi:10.1117/1.JMI.5.1.015501.
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  - Sturgeon, Gregory M., et al. “Synthetic breast phantoms from patient based eigenbreasts.” Med Phys, vol. 44, no. 12, Dec. 2017, pp. 6270–79. Pubmed, doi:10.1002/mp.12579.
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  - Shi, Bibo, et al. “Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?” Acad Radiol, vol. 24, no. 9, Sept. 2017, pp. 1139–47. Pubmed, doi:10.1016/j.acra.2017.03.013.
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  - Ikejimba, Lynda C., et al. “A novel physical anthropomorphic breast phantom for 2D and 3D x-ray imaging.” Med Phys, vol. 44, no. 2, Feb. 2017, pp. 407–16. Pubmed, doi:10.1002/mp.12062.
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  - Ikejimba, Lynda C., et al. “Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms.” Med Phys, vol. 43, no. 10, Oct. 2016, p. 5593. Pubmed, doi:10.1118/1.4962475.
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  - Wang, M., et al. “Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.” Expert Systems With Applications, vol. 56, Sept. 2016, pp. 1–8. Scopus, doi:10.1016/j.eswa.2016.01.053.
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  - Kiarashi, Nooshin, et al. “Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study.” J Med Imaging (Bellingham), vol. 3, no. 3, July 2016, p. 035504. Pubmed, doi:10.1117/1.JMI.3.3.035504.
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  - Sturgeon, Gregory M., et al. “Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation.” Med Phys, vol. 43, no. 5, May 2016, p. 2207. Pubmed, doi:10.1118/1.4945275.
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  - Ikejimba, Lynda, et al. “A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom.” Med Phys, vol. 43, no. 4, Apr. 2016, p. 1627. Pubmed, doi:10.1118/1.4943373.
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  - Grimm, Lars J., et al. “Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.” J Am Coll Radiol, vol. 13, no. 2, Feb. 2016, pp. 198–202. Pubmed, doi:10.1016/j.jacr.2015.09.025.
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  - Erickson, David W., et al. “Population of 224 realistic human subject-based computational breast phantoms.” Med Phys, vol. 43, no. 1, Jan. 2016, p. 23. Pubmed, doi:10.1118/1.4937597.
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  - Schmidt, Matthew, et al. “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, vol. 42, no. 8, Aug. 2015, pp. 4428–34. Pubmed, doi:10.1118/1.4923174.
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  - Kiarashi, Nooshin, et al. “Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data.” Med Phys, vol. 42, no. 7, July 2015, pp. 4116–26. Pubmed, doi:10.1118/1.4919771.
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  - Zhang, Jing, et al. “Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis?” J Am Coll Radiol, vol. 12, no. 7, July 2015, pp. 728–32. Pubmed, doi:10.1016/j.jacr.2015.02.025.
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  - Sechopoulos, Ioannis, et al. “Radiation dosimetry in digital breast tomosynthesis: report of AAPM Tomosynthesis Subcommittee Task Group 223.” Med Phys, vol. 41, no. 9, Sept. 2014, p. 091501. Pubmed, doi:10.1118/1.4892600.
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  - Zhang, Jing, et al. “Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.” Med Phys, vol. 41, no. 9, Sept. 2014, p. 091907. Pubmed, doi:10.1118/1.4892173.
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  - Kiarashi, Nooshin, et al. “Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems.” Ieee Trans Med Imaging, vol. 33, no. 7, July 2014, pp. 1401–09. Pubmed, doi:10.1109/TMI.2014.2312733.
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  - Good, David, et al. “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, vol. 87, no. 1, Sept. 2013, pp. 176–81. Pubmed, doi:10.1016/j.ijrobp.2013.03.015.
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  - Huo, Zhimin, et al. “Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.” Med Phys, vol. 40, no. 7, July 2013, p. 077001. Pubmed, doi:10.1118/1.4807642.
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  - Chung, H., et al. “Estimating breast density with dual energy mammography: A simple model based on calibration phantoms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 8668, June 2013. Scopus, doi:10.1117/12.2008398.
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  - Kiarashi, N., et al. “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), vol. 7361 LNCS, Aug. 2012, pp. 658–65. Scopus, doi:10.1007/978-3-642-31271-7_85.
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  - Baker, Jay A., and Joseph Y. Lo. “Breast tomosynthesis: state-of-the-art and review of the literature.” Acad Radiol, vol. 18, no. 10, Oct. 2011, pp. 1298–310. Pubmed, doi:10.1016/j.acra.2011.06.011.
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  - Mazurowski, Maciej A., et al. “Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis.” J Biomed Inform, vol. 44, no. 5, Oct. 2011, pp. 815–23. Pubmed, doi:10.1016/j.jbi.2011.04.008.
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  - Shafer, C. M., et al. “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, July 2011. Scopus, doi:10.1109/BSEC.2011.5872317.
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  - Chanyavanich, Vorakarn, et al. “Knowledge-based IMRT treatment planning for prostate cancer.” Med Phys, vol. 38, no. 5, May 2011, pp. 2515–22. Pubmed, doi:10.1118/1.3574874.
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  - Webb, Lincoln J., et al. “Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection.” Med Phys, vol. 38, no. 4, Apr. 2011, pp. 1972–80. Pubmed, doi:10.1118/1.3562901.
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  - Singh, Swatee, et al. “Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.” Radiology, vol. 258, no. 1, Jan. 2011, pp. 73–80. Pubmed, doi:10.1148/radiol.10081308.
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  - Robinson, M. Dirk, et al. “Efﬁcient fourier-wavelet super-resolution.” Ieee Trans Image Process, vol. 19, no. 10, Oct. 2010, pp. 2669–81. Pubmed, doi:10.1109/TIP.2010.2050107.
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  - Ranger, Nicole T., et al. “A technique optimization protocol and the potential for dose reduction in digital mammography.” Med Phys, vol. 37, no. 3, Mar. 2010, pp. 962–69. Pubmed, doi:10.1118/1.3276732.
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  - Shafer, Christina M., et al. “The quantitative potential for breast tomosynthesis imaging.” Med Phys, vol. 37, no. 3, Mar. 2010, pp. 1004–16. Pubmed, doi:10.1118/1.3285038.
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  - Chawla, Amarpreet S., et al. “Optimized image acquisition for breast tomosynthesis in projection and reconstruction space.” Med Phys, vol. 36, no. 11, Nov. 2009, pp. 4859–69. Pubmed, doi:10.1118/1.3231814.
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  - Saunders, Robert S., et al. “Can compression be reduced for breast tomosynthesis? Monte carlo study on mass and microcalcification conspicuity in tomosynthesis.” Radiology, vol. 251, no. 3, June 2009, pp. 673–82. Pubmed, doi:10.1148/radiol.2521081278.
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  - Jesneck, Jonathan L., et al. “Do serum biomarkers really measure breast cancer?” Bmc Cancer, vol. 9, May 2009, p. 164. Pubmed, doi:10.1186/1471-2407-9-164.
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  - Chawla, Amarpreet S., et al. “Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer.” Acad Radiol, vol. 16, no. 4, Apr. 2009, pp. 456–63. Pubmed, doi:10.1016/j.acra.2008.09.013.
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  - Singh, Swatee, et al. “Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach.” Med Phys, vol. 35, no. 8, Aug. 2008, pp. 3626–36. Pubmed, doi:10.1118/1.2953562.
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  - Williams, Mark B., et al. “Optimization of exposure parameters in full field digital mammography.” Med Phys, vol. 35, no. 6, June 2008, pp. 2414–23. Pubmed, doi:10.1118/1.2912177.
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  - Floyd, Carey E., et al. “Neutron-stimulated emission computed tomography of a multi-element phantom.” Phys Med Biol, vol. 53, no. 9, May 2008, pp. 2313–26. Pubmed, doi:10.1088/0031-9155/53/9/008.
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  - Xia, Jessie Q., et al. “Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique.” Med Phys, vol. 35, no. 5, May 2008, pp. 1950–58. Pubmed, doi:10.1118/1.2903436.
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  - Chawla, Amarpreet S., et al. “A mathematical model platform for optimizing a multiprojection breast imaging system.” Med Phys, vol. 35, no. 4, Apr. 2008, pp. 1337–45. Pubmed, doi:10.1118/1.2885367.
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  - Mazurowski, Maciej A., et al. “Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.” Neural Netw, vol. 21, no. 2–3, Mar. 2008, pp. 427–36. Pubmed, doi:10.1016/j.neunet.2007.12.031.
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  - Karellas, Andrew, et al. “Point/Counterpoint. Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer.” Med Phys, vol. 35, no. 2, Feb. 2008, pp. 409–11. Pubmed, doi:10.1118/1.2825612.
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  - Karellas, Andrew, et al. “Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer.” Medical Physics, vol. 35, no. 2, WILEY, Feb. 2008, pp. 409–11. Wos, doi:10.1118/1.2825612.
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  - Chen, Ying, et al. “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, vol. 1, no. 2, 2008, pp. 189–204. Pubmed, doi:10.1504/IJFIPM.2008.020187.
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  - Chen, Y., et al. “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, Dec. 2007, pp. 563–69. Scopus, doi:10.1109/BIBE.2007.4375617.
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  - Singh, S., et al. “Breast mass detection in tomosynthesis projection images using information-theoretic similarity measures.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6514, no. PART 1, Oct. 2007. Scopus, doi:10.1117/12.713004.
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  - Chen, Ying, et al. “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, vol. 34, no. 10, Oct. 2007, pp. 3885–92. Pubmed, doi:10.1118/1.2776256.
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  - Jesneck, Jonathan L., et al. “Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors.” Radiology, vol. 244, no. 2, Aug. 2007, pp. 390–98. Pubmed, doi:10.1148/radiol.2442060712.
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  - Tourassi, Georgia D., et al. “Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance.” Med Phys, vol. 34, no. 8, Aug. 2007, pp. 3193–204. Pubmed, doi:10.1118/1.2751075.
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  - Baydush, Alan H., et al. “Incorporation of a Laguerre-Gauss channelized Hotelling observer for false-positive reduction in a mammographic mass CAD system.” J Digit Imaging, vol. 20, no. 2, June 2007, pp. 196–202. Pubmed, doi:10.1007/s10278-007-9009-8.
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  - Samei, Ehsan, et al. “Multiprojection correlation imaging for improved detection of pulmonary nodules.” Ajr Am J Roentgenol, vol. 188, no. 5, May 2007, pp. 1239–45. Pubmed, doi:10.2214/AJR.06.0843.
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  - Floyd, C. E., et al. “Neutron stimulated emission computed tomography: Background corrections.” Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions With Materials and Atoms, vol. 254, no. 2, Jan. 2007, pp. 329–36. Scopus, doi:10.1016/j.nimb.2006.11.098.
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  - Tourassi, Georgia D., et al. “Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.” Med Phys, vol. 34, no. 1, Jan. 2007, pp. 140–50. Pubmed, doi:10.1118/1.2401667.
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  - Singh, S, Tourassi, GD, and Lo, JY. "Breast mass detection in tomosynthesis projection images using information-theoretic similarity measures." MEDICAL IMAGING 2007: COMPUTER-AIDED DIAGNOSIS, PTS 1 AND 2 6514 (2007). Full Text Link to Item
  - Jesneck, Jonathan L., et al. “Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.” Med Phys, vol. 33, no. 8, Aug. 2006, pp. 2945–54. Pubmed, doi:10.1118/1.2208934.
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  - Floyd, Carey E., et al. “Introduction to neutron stimulated emission computed tomography.” Phys Med Biol, vol. 51, no. 14, July 2006, pp. 3375–90. Pubmed, doi:10.1088/0031-9155/51/14/006.
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  - Sharma, A., et al. “Rotating slat collimator design for high-energy near-field imaging.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6142 I, July 2006. Scopus, doi:10.1117/12.653929.
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  - Chen, Y., et al. “Noise power spectrum analysis for several digital breast tomosynthesis reconstruction algorithms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6142 III, June 2006. Scopus, doi:10.1117/12.652282.
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  - Chen, Y., et al. “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, vol. 6142 I, June 2006. Scopus, doi:10.1117/12.652264.
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  - Floyd, C. E., et al. “Breast cancer diagnosis using neutron stimulated emission computed tomography: Dose and count requirements.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6142 II, June 2006. Scopus, doi:10.1117/12.656045.
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  - Jesneck, J. L., et al. “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, vol. 6146, June 2006. Scopus, doi:10.1117/12.655235.
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  - Singh, S., et al. “Mass detection in mammographic ROIs using Watson filters.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6146, June 2006. Scopus, doi:10.1117/12.653224.
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  - Williams, M. B., et al. “Beam optimization for digital mammography - II.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4046 LNCS, Jan. 2006, pp. 273–80.
  - Bissonnette, M., et al. “Digital breast tomosynthesis using an amorphous selenium flat panel detector.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 5745, no. I, Aug. 2005, pp. 529–40. Scopus, doi:10.1117/12.601622.
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  - Chen, Y., et al. “Impulse response analysis for several digital tomosynthesis mammography reconstruction algorithms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 5745, no. I, Aug. 2005, pp. 541–49. Scopus, doi:10.1117/12.595684.
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  - Jesneck, J. L., et al. “Detector evaluation of a prototype amorphous selenium-based full field digital mammography system.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 5745, no. I, Aug. 2005, pp. 478–85. Scopus, doi:10.1117/12.596087.
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  - Markey, M. K., and J. Y. Lo. “Issues in assessing multi-institutional performance of BI-RADS-based CAD systems.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 5747, no. II, Aug. 2005, pp. 858–65. Scopus, doi:10.1117/12.594706.
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  - Xia, J. Q., et al. “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, vol. 5745, no. II, Aug. 2005, pp. 1096–102. Scopus, doi:10.1117/12.595960.
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  - Bilska-Wolak, Anna O., et al. “Computer aid for decision to biopsy breast masses on mammography: validation on new cases.” Acad Radiol, vol. 12, no. 6, June 2005, pp. 671–80. Pubmed, doi:10.1016/j.acra.2005.02.011.
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  - Samei, Ehsan, et al. “Comparative scatter and dose performance of slot-scan and full-field digital chest radiography systems.” Radiology, vol. 235, no. 3, June 2005, pp. 940–49. Pubmed, doi:10.1148/radiol.2353040516.
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  - Baker, Jay A., et al. “Accuracy of segmentation of a commercial computer-aided detection system for mammography.” Radiology, vol. 235, no. 2, May 2005, pp. 385–90. Pubmed, doi:10.1148/radiol.2352040899.
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  - Saunders, Robert S., et al. “Physical characterization of a prototype selenium-based full field digital mammography detector.” Med Phys, vol. 32, no. 2, Feb. 2005, pp. 588–99. Pubmed, doi:10.1118/1.1855033.
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  - Samei, Ehsan, et al. “A framework for optimising the radiographic technique in digital X-ray imaging.” Radiat Prot Dosimetry, vol. 114, no. 1–3, 2005, pp. 220–29. Pubmed, doi:10.1093/rpd/nch562.
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  - Fischer, E. A., et al. “Bayesian networks of BI-RADS™ descriptors for breast lesion Classification.” Annual International Conference of the Ieee Engineering in Medicine and Biology Proceedings, vol. 26 IV, Dec. 2004, pp. 3031–34.
  - Baker, Jay A., et al. “Computer-aided detection in screening mammography: variability in cues.” Radiology, vol. 233, no. 2, Nov. 2004, pp. 411–17. Pubmed, doi:10.1148/radiol.2332031200.
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  - Land, W. H., et al. “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, vol. 5370 II, Oct. 2004, pp. 777–84. Scopus, doi:10.1117/12.533142.
    Full Text
  - Land, W. H., et al. “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, vol. 5370 II, Oct. 2004, pp. 880–87. Scopus, doi:10.1117/12.535864.
    Full Text
  - Samei, Ehsan, et al. “Fundamental imaging characteristics of a slot-scan digital chest radiographic system.” Med Phys, vol. 31, no. 9, Sept. 2004, pp. 2687–98. Pubmed, doi:10.1118/1.1783531.
    Full Text Link to Item
  - Baker, Jay A., et al. “Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion.” Ajr Am J Roentgenol, vol. 181, no. 4, Oct. 2003, pp. 1083–88. Pubmed, doi:10.2214/ajr.181.4.1811083.
    Full Text Link to Item
  - Bilska-Wolak, A. O., et al. “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, vol. 5032 III, Sept. 2003, pp. 1386–91. Scopus, doi:10.1117/12.481349.
    Full Text
  - Land, W. H., et al. “Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions.” Proceedings of Spie the International Society for Optical Engineering, vol. 5032 II, Sept. 2003, pp. 898–907. Scopus, doi:10.1117/12.483550.
    Full Text
  - Land, W. H., et al. “Application of support vector machines to breast cancer screening using mammogram and clinical history data.” Proceedings of Spie the International Society for Optical Engineering, vol. 5032 I, Sept. 2003, pp. 546–56. Scopus, doi:10.1117/12.480235.
    Full Text
  - Lo, J. Y., et al. “Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists.” Proceedings of Spie the International Society for Optical Engineering, vol. 5032 II, Sept. 2003, pp. 882–89. Scopus, doi:10.1117/12.480869.
    Full Text
  - Tourassi, G. D., et al. “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, vol. 5032 I, Sept. 2003, pp. 207–14. Scopus, doi:10.1117/12.481111.
    Full Text
  - Bilska-Wolak, Anna O., et al. “Application of likelihood ratio to classification of mammographic masses; performance comparison to case-based reasoning.” Med Phys, vol. 30, no. 5, May 2003, pp. 949–58. Pubmed, doi:10.1118/1.1565339.
    Full Text Link to Item
  - Markey, Mia K., et al. “Self-organizing map for cluster analysis of a breast cancer database.” Artif Intell Med, vol. 27, no. 2, Feb. 2003, pp. 113–27. Pubmed, doi:10.1016/s0933-3657(03)00003-4.
    Full Text Link to Item
  - Land, W. H., et al. “Improving mammogram screening using a bank of support vector machines (SVMs).” Intelligent Engineering Systems Through Artificial Neural Networks, vol. 12, Dec. 2002, pp. 779–84.
  - Land, W. H., et al. “Using evolutionary programming to configure support vector machines for the diagnosis of breast cancer.” Intelligent Engineering Systems Through Artificial Neural Networks, vol. 12, Dec. 2002, pp. 249–54.
  - Markey, Mia K., et al. “Differences between computer-aided diagnosis of breast masses and that of calcifications.” Radiology, vol. 223, no. 2, May 2002, pp. 489–93. Pubmed, doi:10.1148/radiol.2232011257.
    Full Text Link to Item
  - Gavrielides, Marios A., et al. “Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms.” Med Phys, vol. 29, no. 4, Apr. 2002, pp. 475–83. Pubmed, doi:10.1118/1.1460874.
    Full Text Link to Item
  - Keogan, Mary T., et al. “Outcome analysis of patients with acute pancreatitis by using an artificial neural network.” Acad Radiol, vol. 9, no. 4, Apr. 2002, pp. 410–19. Pubmed, doi:10.1016/s1076-6332(03)80186-1.
    Full Text Link to Item
  - Markey, Mia K., et al. “Perceptron error surface analysis: a case study in breast cancer diagnosis.” Comput Biol Med, vol. 32, no. 2, Mar. 2002, pp. 99–109. Pubmed, doi:10.1016/s0010-4825(01)00035-x.
    Full Text Link to Item
  - Lo, Joseph Y., et al. “Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer.” Ajr Am J Roentgenol, vol. 178, no. 2, Feb. 2002, pp. 457–63. Pubmed, doi:10.2214/ajr.178.2.1780457.
    Full Text Link to Item
  - Land, W. H., et al. “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, vol. 1, Jan. 2002, pp. 187–92. Scopus, doi:10.1109/CEC.2002.1006231.
    Full Text
  - Land, W. H., et al. “Application of support vector machines to breast cancer screening using mammogram and history data.” Proceedings of Spie the International Society for Optical Engineering, vol. 4684 I, Jan. 2002, pp. 636–42. Scopus, doi:10.1117/12.467206.
    Full Text
  - Markey, M. K., et al. “Cluster analysis of BI-RADS™ descriptions of biopsy-proven breast lesions.” Proceedings of Spie the International Society for Optical Engineering, vol. 4684 I, Jan. 2002, pp. 363–70. Scopus, doi:10.1117/12.467177.
    Full Text
  - Baydush, A. H., et al. “Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers.” Med Phys, vol. 28, no. 12, Dec. 2001, pp. 2403–09. Pubmed, doi:10.1118/1.1420402.
    Full Text Link to Item
  - Tourassi, G. D., et al. “A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.” Med Phys, vol. 28, no. 5, May 2001, pp. 804–11. Pubmed, doi:10.1118/1.1367861.
    Full Text Link to Item
  - Land, J., et al. “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, vol. 4322, no. 3, Jan. 2001, pp. 1717–24. Scopus, doi:10.1117/12.431058.
    Full Text
  - Land, J., et al. “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, vol. 2, Jan. 2001, pp. 1147–54.
  - Land, W. H., et al. “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, vol. 3979, Dec. 2000.
  - Land, W., et al. “Application of a new Evolutionary Programming/Adaptive Boosting hybrid to breast cancer diagnosis.” Proceedings of the Ieee Conference on Evolutionary Computation, Icec, vol. 2, Dec. 2000, pp. 1436–42.
  - Floyd, C. E., et al. “Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions.” Ajr Am J Roentgenol, vol. 175, no. 5, Nov. 2000, pp. 1347–52. Pubmed, doi:10.2214/ajr.175.5.1751347.
    Full Text Link to Item
  - Tourassi, Georgia D., et al. “Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation.” Medical Imaging 2000: Image Processing, SPIE, June 2000. Crossref, doi:10.1117/12.387680.
    Full Text
  - Gavrielides, M. A., et al. “Segmentation of suspicious clustered microcalcifications in mammograms.” Med Phys, vol. 27, no. 1, Jan. 2000, pp. 13–22. Pubmed, doi:10.1118/1.598852.
    Full Text Link to Item
  - Jr, W. H. L., et al. “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, vol. 3979, 2000.
  - Lo, J. Y., et al. “Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis.” Proceedings of Spie the International Society for Optical Engineering, vol. 3979, Jan. 2000.
  - Lo, J. Y., et al. “Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis.” Proceedings of Spie the International Society for Optical Engineering, vol. 3979, 2000.
  - Tourassi, G. D., et al. “Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation.” Proceedings of Spie the International Society for Optical Engineering, vol. 3979, Jan. 2000.
  - Land, W. H., et al. “Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms.” Intelligent Engineering Systems Through Artificial Neural Networks, vol. 9, Dec. 1999, pp. 803–08.
  - Lo, J. Y., et al. “Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis.” Proceedings of the International Joint Conference on Neural Networks, vol. 5, Dec. 1999, pp. 3712–16.
  - Tourassi, G. D., et al. “Constraint Satisfaction Neural Network for medical diagnosis.” Proceedings of the International Joint Conference on Neural Networks, vol. 5, Dec. 1999, pp. 3632–35.
  - Munley, M. T., et al. “A neural network to predict symptomatic lung injury.” Phys Med Biol, vol. 44, no. 9, Sept. 1999, pp. 2241–49. Pubmed, doi:10.1088/0031-9155/44/9/311.
    Full Text Link to Item
  - Floyd, C. E., et al. “Case-based reasoning as a computer aid to diagnosis.” Proceedings of Spie the International Society for Optical Engineering, vol. 3661, no. I, Jan. 1999, pp. 486–89.
  - Floyd, C. E., et al. “Prediction of breast biopsy outcomes from mammographic findings.” Computer Aided Diagnosis in Medical Imaging, edited by K. Doi et al., vol. 1182, ELSEVIER SCIENCE BV, Jan. 1999, pp. 193–200.
    Link to Item
  - Lo, J. Y., and C. E. Floyd. “Application of artificial neural networks for diagnosis of breast cancer.” Proceedings of the 1999 Congress on Evolutionary Computation, Cec 1999, vol. 3, Jan. 1999, pp. 1755–59. Scopus, doi:10.1109/CEC.1999.785486.
    Full Text
  - Lo, J. Y., et al. “Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks.” Acad Radiol, vol. 6, no. 1, Jan. 1999, pp. 10–15. Pubmed, doi:10.1016/s1076-6332(99)80056-7.
    Full Text Link to Item
  - Lo, JY, and Floyd, CE. "Computer-aided diagnosis of breast cancer." COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING 1182 (1999): 221-225. Link to Item
  - Freed, K. S., et al. “Predictive model for the diagnosis of intraabdominal abscess.” Acad Radiol, vol. 5, no. 7, July 1998, pp. 473–79. Pubmed, doi:10.1016/s1076-6332(98)80187-6.
    Full Text Link to Item
  - Sakamoto, H., et al. “QoS middleware for Internet multimedia streaming.” Nec Tech. J. (Japan), vol. 51, no. 8, 1998, pp. 35–40.
  - Lo, J. Y., and C. E. Floyd. “Self-organizing maps for analyzing mammographic findings.” Ieee International Conference on Neural Networks Conference Proceedings, vol. 4, Dec. 1997, pp. 2472–74. Scopus, doi:10.1109/ICNN.1997.614546.
    Full Text
  - Lo, J. Y., et al. “Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.” Radiology, vol. 203, no. 1, Apr. 1997, pp. 159–63. Pubmed, doi:10.1148/radiology.203.1.9122385.
    Full Text Link to Item
  - Lo, J. Y., et al. “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, vol. 2710, Dec. 1996, pp. 725–32. Scopus, doi:10.1117/12.237977.
    Full Text
  - Floyd, C. E., et al. “Diffuse nodular lung disease on chest radiographs: a pilot study of characterization by fractal dimension.” Ajr Am J Roentgenol, vol. 167, no. 5, Nov. 1996, pp. 1185–87. Pubmed, doi:10.2214/ajr.167.5.8911177.
    Full Text Link to Item
  - Baker, J. A., et al. “Artificial neural network: improving the quality of breast biopsy recommendations.” Radiology, vol. 198, no. 1, Jan. 1996, pp. 131–35. Pubmed, doi:10.1148/radiology.198.1.8539365.
    Full Text Link to Item
  - Jr, Floyd C. E., et al. “Computer aided diagnosis in thoracic and mammographic radiology.” Med. Imaging Technol. (Japan), vol. 14, no. 6, 1996, pp. 629–34.
  - Mun, S. K., et al. “Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography.” Proceedings of Spie the International Society for Optical Engineering, vol. 2431, Dec. 1995, pp. 442–46.
  - Lo, J. Y., et al. “Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features.” Acad Radiol, vol. 2, no. 10, Oct. 1995, pp. 841–50. Pubmed, doi:10.1016/s1076-6332(05)80057-1.
    Full Text Link to Item
  - Baker, J. A., et al. “Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.” Radiology, vol. 196, no. 3, Sept. 1995, pp. 817–22. Pubmed, doi:10.1148/radiology.196.3.7644649.
    Full Text Link to Item
  - Lo, J. Y., et al. “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, vol. 2434, May 1995, pp. 571–78. Scopus, doi:10.1117/12.208729.
    Full Text
  - Floyd, C. E., et al. “Prediction of breast cancer malignancy using an artificial neural network.” Cancer, vol. 74, no. 11, Dec. 1994, pp. 2944–48. Pubmed, doi:10.1002/1097-0142(19941201)74:11<2944::aid-cncr2820741109>3.0.co;2-f.
    Full Text Link to Item
  - Floyd, C. E., et al. “Bayesian restoration of chest radiographs. Scatter compensation with improved signal-to-noise ratio.” Invest Radiol, vol. 29, no. 10, Oct. 1994, pp. 904–10. Pubmed, doi:10.1097/00004424-199410000-00007.
    Full Text Link to Item
  - Lo, J. Y., et al. “Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network.” Proceedings of Spie the International Society for Optical Engineering, vol. 2167, May 1994, pp. 601–11. Scopus, doi:10.1117/12.175095.
    Full Text
  - Lo, J. Y., et al. “Scatter compensation in digital chest radiography using the posterior beam stop technique.” Med Phys, vol. 21, no. 3, Mar. 1994, pp. 435–43. Pubmed, doi:10.1118/1.597388.
    Full Text Link to Item
  - Baker, J. A., et al. “Observer evaluation of scatter subtraction for digital portable chest radiographs.” Invest Radiol, vol. 28, no. 8, Aug. 1993, pp. 667–70. Pubmed, doi:10.1097/00004424-199308000-00001.
    Full Text Link to Item
  - Jordan, L. K., et al. “Measurement of scatter fractions in erect posteroanterior and lateral chest radiography.” Radiology, vol. 188, no. 1, July 1993, pp. 215–18. Pubmed, doi:10.1148/radiology.188.1.8511301.
    Full Text Link to Item
  - Lo, J. Y., et al. “An artificial neural network for estimating scatter exposures in portable chest radiography.” Med Phys, vol. 20, no. 4, July 1993, pp. 965–73. Pubmed, doi:10.1118/1.596978.
    Full Text Link to Item
  - Floyd, C. E., et al. “Scatter compensation for digital chest radiography using maximum likelihood expectation maximization.” Invest Radiol, vol. 28, no. 5, May 1993, pp. 427–33. Pubmed, doi:10.1097/00004424-199305000-00009.
    Full Text Link to Item
  - BAYDUSH, A. H., et al. “SCATTER REDUCTION IN PORTABLE DIGITAL CHEST RADIOGRAPHY WITH BAYESIAN IMAGE ESTIMATION.” Radiology, vol. 185, RADIOLOGICAL SOC NORTH AMER, Nov. 1992, pp. 305–305.
    Link to Item
  - LO, J. Y., et al. “SPATIALLY VARYING SCATTER ESTIMATION IN PORTABLE CHEST RADIOGRAPHY WITH AN ARTIFICIAL NEURAL NETWORK.” Radiology, vol. 185, RADIOLOGICAL SOC NORTH AMER, Nov. 1992, pp. 300–300.
    Link to Item
  - Floyd, C. E., et al. “Measurement of scatter fractions in clinical bedside radiography.” Radiology, vol. 183, no. 3, June 1992, pp. 857–61. Pubmed, doi:10.1148/radiology.183.3.1584947.
    Full Text Link to Item
  - Floyd, C. E., et al. “Posterior beam-stop method for scatter fraction measurement in digital radiography.” Invest Radiol, vol. 27, no. 2, Feb. 1992, pp. 119–23. Pubmed, doi:10.1097/00004424-199202000-00004.
    Full Text Link to Item
  - Floyd, C. E., et al. “Quantitative scatter measurement in digital radiography using a photostimulable phosphor imaging system.” Med Phys, vol. 18, no. 3, May 1991, pp. 408–13. Pubmed, doi:10.1118/1.596687.
    Full Text Link to Item
  - Chotas, H. G., et al. “Scatter fractions in AMBER imaging.” Radiology, vol. 177, no. 3, Dec. 1990, pp. 879–80. Pubmed, doi:10.1148/radiology.177.3.2244003.
    Full Text Link to Item
- Book Sections
  - Robinson, M. D., et al. “New applications of super-resolution in medical imaging.” Super-Resolution Imaging, 2017, pp. 383–412. Scopus, doi:10.1201/9781439819319.
    Full Text
  - Robinson, M. D., et al. Efficient restoration and enhancement of super-resolved X-ray images. 2008, pp. 629–32. Scopus, doi:10.1109/ICIP.2008.4711833.
    Full Text
  - Jesneck, J. L., et al. Decision fusion of circulating markers for breast cancer detection in premenopausal women. 2007, pp. 1434–38. Scopus, doi:10.1109/BIBE.2007.4375762.
    Full Text
  - Land, W. H., et al. “Using computational intelligence for computer-aided diagnosis of screen-film mammograms.” Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, 2006, pp. 315–75. Scopus, doi:10.1117/3.651880.ch10.
    Full Text
  - Lo, J. Y., et al. “Computer-aided diagnosis in breast imaging: Where do we go after detection?” Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer, 2006, pp. 871–900. Scopus, doi:10.1117/3.651880.ch27.
    Full Text
- Other Articles
  - Robinson, M. D., et al. “Efficient registration of aliased x-ray images.” Conference Record Asilomar Conference on Signals, Systems and Computers, 1 Dec. 2007, pp. 215–19. Scopus, doi:10.1109/ACSSC.2007.4487198.
    Full Text
- Conference Papers
  - Benitez, M., et al. “Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2512886.
    Full Text
  - Fu, W., et al. “Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2512883.
    Full Text
  - Geng, Y., et al. “2.5D CNN model for detecting lung disease using weak supervision.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513631.
    Full Text
  - Han, S., et al. “Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513577.
    Full Text
  - Hou, R., et al. “Malignant microcalcification clusters detection using unsupervised deep autoencoders.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2512829.
    Full Text
  - Kong, D., et al. “Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513125.
    Full Text
  - Liu, Y., et al. “Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10954, 2019. Scopus, doi:10.1117/12.2512887.
    Full Text
  - Pegues, H., et al. “Using inkjet 3D printing to create contrast-enhanced textured physical phantoms for CT.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2512890.
    Full Text
  - Ren, Y., et al. “Multiview mammographic mass detection based on a single shot detection system.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513136.
    Full Text
  - Ren, Y., et al. “Mask Embedding for Realistic High-Resolution Medical Image Synthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11769 LNCS, 2019, pp. 422–30. Scopus, doi:10.1007/978-3-030-32226-7_47.
    Full Text
  - Tang, R., et al. “Classification of chest CT using case-level weak supervision.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513576.
    Full Text
  - Tong, H., et al. “Controlling the position-dependent contrast of 3D printed physical phantoms with a single material.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10948, 2019. Scopus, doi:10.1117/12.2513469.
    Full Text
  - Lo, Joseph Y., et al. “Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features.” Cancer Research, vol. 78, no. 4, AMER ASSOC CANCER RESEARCH, 2018.
    Link to Item
  - Hou, R., et al. “Improving classification with forced labeling of other related classes: Application to prediction of upstaged ductal carcinoma in situ using mammographic features.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10575, 2018. Scopus, doi:10.1117/12.2293809.
    Full Text
  - Ikejimba, L. C., et al. “Methodology for the objective assessment of lesion detection performance with breast tomosynthesis and digital mammography using a physical anthropomorphic phantom.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10573, 2018. Scopus, doi:10.1117/12.2294562.
    Full Text
  - Ikejimba, L. C., et al. “Method for task-based evaluation of clinical FFDM and DBT systems using an anthropomorphic breast phantom.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10718, 2018. Scopus, doi:10.1117/12.2318523.
    Full Text
  - Rajagopal, J., et al. “Evaluation of statistical breast phantoms with higher resolution.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10573, 2018. Scopus, doi:10.1117/12.2294049.
    Full Text
  - Rossman, A., et al. “3D printed anthropomorphic physical phantom for mammography and DBT with high contrast custom materials, lesions, and uniform chest wall region.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10573, 2018. Scopus, doi:10.1117/12.2294519.
    Full Text
  - Shi, B., et al. “Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10575, 2018. Scopus, doi:10.1117/12.2293594.
    Full Text
  - Wei, Q., et al. “Anomaly detection for medical images based on a one-class classification.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10575, 2018. Scopus, doi:10.1117/12.2293408.
    Full Text
  - Chen, X., et al. “High-resolution, anthropomorphic, computational breast phantom: Fusion of rule-based structures with patient-based anatomy.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10132, 2017. Scopus, doi:10.1117/12.2255913.
    Full Text
  - Ikejimba, L. C., et al. “A physical breast phantom for 2D and 3D x-ray imaging made through inkjet printing.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10132, 2017. Scopus, doi:10.1117/12.2255016.
    Full Text
  - Reinhold, J., et al. “Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: A model observer study with anthropomorphic computational breast phantoms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10136, 2017. Scopus, doi:10.1117/12.2255909.
    Full Text
  - Sauer, T. J., et al. “Detectability of artificial lesions in anthropomorphic virtual breast phantoms of variable glandular fraction.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10132, 2017. Scopus, doi:10.1117/12.2255896.
    Full Text
  - Shi, B., et al. “Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10134, 2017. Scopus, doi:10.1117/12.2255731.
    Full Text
  - Shi, B., et al. “Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10134, 2017. Scopus, doi:10.1117/12.2255847.
    Full Text
  - Zhao, A., et al. “Comparison of effects of dose on image quality in digital breast tomosynthesis across multiple vendors.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10132, 2017. Scopus, doi:10.1117/12.2255570.
    Full Text
  - Zhao, C., et al. “Third generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and uniform chest wall QC region.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10132, 2017. Scopus, doi:10.1117/12.2256091.
    Full Text
  - Ikejimba, L., et al. “Comparison of model and human observer performance in FFDM, DBT, and synthetic mammography.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9783, 2016. Scopus, doi:10.1117/12.2216858.
    Full Text
  - Makeev, A., et al. “Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9783, 2016. Scopus, doi:10.1117/12.2217438.
    Full Text
  - Sikaria, D., et al. “Second generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and inkjet printing of iodinated lesion inserts.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9783, 2016. Scopus, doi:10.1117/12.2217667.
    Full Text
  - Solomon, J., et al. “Design, fabrication, and implementation of voxel-based 3D printed textured phantoms for task-based image quality assessment in CT.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9783, 2016. Scopus, doi:10.1117/12.2217463.
    Full Text
  - Sturgeon, G. M., et al. “Eigenbreasts for statistical breast phantoms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9783, 2016. Scopus, doi:10.1117/12.2216398.
    Full Text
  - Wang, M., et al. “Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9787, 2016. Scopus, doi:10.1117/12.2201061.
    Full Text
  - Grimm, L. J., et al. “Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality?” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9416, 2015. Scopus, doi:10.1117/12.2082810.
    Full Text
  - Ikejimba, L., et al. “A quantitative metrology for performance characterization of breast tomosynthesis systems based on an anthropomorphic phantom.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9412, 2015. Scopus, doi:10.1117/12.2082594.
    Full Text
  - Kiarashi, N., et al. “The impact of breast structure on lesion detection in breast tomosynthesis.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9412, 2015. Scopus, doi:10.1117/12.2082473.
    Full Text
  - Ikejimba, Lynda C., et al. “Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis.” Med Phys, vol. 41, no. 6, 2014, p. 061908. Pubmed, doi:10.1118/1.4873317.
    Full Text Link to Item
  - Mahadevan, R., et al. “A task-based comparison of two reconstruction algorithms for digital breast tomosynthesis.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9033, 2014. Scopus, doi:10.1117/12.2043829.
    Full Text
  - Mazurowski, M. A., et al. “Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9037, 2014. Scopus, doi:10.1117/12.2044404.
    Full Text
  - Nolte, A., et al. “A second generation of physical anthropomorphic 3D breast phantoms based on human subject data.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9033, 2014. Scopus, doi:10.1117/12.2043703.
    Full Text
  - Segars, W. P., et al. “Population of 100 realistic, patient-based computerized breast phantoms for multi-modality imaging research.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 9033, 2014. Scopus, doi:10.1117/12.2043868.
    Full Text
  - Kiarashi, N., et al. “Development of matched virtual and physical breast phantoms based on patient data.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 8668, 2013. Scival, doi:10.1117/12.2008406.
    Full Text
  - Chanyavanich, V., et al. “SU-E-T-572: A Plan Quality Metric for Evaluating Knowledge-Based Treatment Plans.” Med Phys, vol. 39, no. 6Part19, 2012, p. 3837. Pubmed, doi:10.1118/1.4735661.
    Full Text Link to Item
  - Dick, D., et al. “WE-G-BRCD-06: Knowledge-Based Intensity Modulated Radiotherapy (IMRT) Treatment Planning for Prostate Cancer.” Med Phys, vol. 39, no. 6Part28, 2012, pp. 3965–66. Pubmed, doi:10.1118/1.4736183.
    Full Text Link to Item
  - Kiarashi, N., et al. “Development of a dynamic 4D anthropomorphic breast phantom for contrast-based breast imaging.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 8313, 2012. Scopus, doi:10.1117/12.913332.
    Full Text
  - Lin, Y., et al. “3D biopsy for tomosynthesis: Simulation of prior information based reconstruction for dose and artifact reduction.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 8313, 2012. Scopus, doi:10.1117/12.912346.
    Full Text
  - Shafer, C. M., et al. “Segmentation of adipose and glandular tissue for breast tomosynthesis imaging using a 3D hidden-Markov model trained on breast MRIs.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 7961, 2011. Scopus, doi:10.1117/12.878137.
    Full Text
  - Chanyavanich, V., et al. “SU‐E‐T‐851: An Inter‐Institutional Comparison of Knowledge‐Based IMRT Treatment Planning for Prostate Cancer.” Medical Physics, vol. 38, no. 6, 2011. Scopus, doi:10.1118/1.3612815.
    Full Text
  - Mazurowski, M. A., et al. “User modeling for improved computer-aided training in radiology: Initial experience.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 7627, 2010. Scopus, doi:10.1117/12.843863.
    Full Text
  - Chanyavanich, V., et al. “SU‐GG‐T‐134: Knowledge‐Based IMRT Treatment Planning for Prostate Cancer.” Medical Physics, vol. 37, no. 6, 2010. Scopus, doi:10.1118/1.3468524.
    Full Text
  - Freeman, M. S., et al. “SU‐GG‐T‐131: A Linear Metric of Knowledge‐Based IMRT Treatment Plan Quality for the Prostate.” Medical Physics, vol. 37, no. 6, 2010. Scopus, doi:10.1118/1.3468521.
    Full Text
  - Mehtaji, D., et al. “SU‐GG‐I‐154: Evaluation of Quantitative Potential of Breast Tomosynthesis Using a Voxelized Anthropomorphic Breast Phantom.” Medical Physics, vol. 37, no. 6, 2010. Scopus, doi:10.1118/1.3468190.
    Full Text
  - lo, J. Y., et al. “WE‐A‐201B‐04: Reducing Dose in Breast Tomosynthesis Using Bayesian Image Estimation.” Medical Physics, vol. 37, no. 6, 2010. Scopus, doi:10.1118/1.3469336.
    Full Text
  - Chawla, A. S., et al. “Optimized lesion detection in breast tomosynthesis.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 7258, 2009. Scopus, doi:10.1117/12.813964.
    Full Text
  - Li, C. M., et al. “Computerized 3D breast phantom with enhanced High-Resolution detail.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 7258, 2009. Scopus, doi:10.1117/12.813529.
    Full Text
  - Shafer, C., et al. “MO‐FF‐A4‐01: Evaluation of Background Trend Correction Technique in Breast Tomosynthesis Quantitation.” Medical Physics, vol. 36, no. 6, 2009. Scopus, doi:10.1118/1.3182295.
    Full Text
  - Xia, J. Q., and J. Y. Lo. “Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal.” 8th Ieee International Conference on Bioinformatics and Bioengineering, Bibe 2008, 2008. Scopus, doi:10.1109/BIBE.2008.4696788.
    Full Text
  - Chawla, A. S., et al. “Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 635–42. Scopus, doi:10.1007/978-3-540-70538-3_88.
    Full Text
  - Shafer, C. M., et al. “Assessment of low energies and slice depth in the quantification of breast tomosynthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 530–36. Scopus, doi:10.1007/978-3-540-70538-3_74.
    Full Text
  - Singh, S., et al. “Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 286–91. Scopus, doi:10.1007/978-3-540-70538-3_40.
    Full Text
  - Tourassi, G. D., et al. “Knowledge transfer across breast cancer screening modalities: A pilot study using an information-theoretic CADe system for mass detection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 292–98. Scopus, doi:10.1007/978-3-540-70538-3_41.
    Full Text
  - Chawla, A. S., et al. “Optimized acquisition scheme for multi-projection correlation imaging of breast cancer.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6915, 2008. Scopus, doi:10.1117/12.773174.
    Full Text
  - Singh, S., et al. “Computer aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6915, 2008. Scopus, doi:10.1117/12.772978.
    Full Text
  - Li, C. M., et al. “Three-dimensional computer generated breast phantom based on empirical data.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6913, 2008. Scopus, doi:10.1117/12.772185.
    Full Text
  - Shafer, C. M., et al. “Toward quantification of breast tomosynthesis imaging.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6913, 2008. Scopus, doi:10.1117/12.772753.
    Full Text
  - Robinson, M. Dirk, et al. “Efficient Restoration and Enhancement of Super-resolved X-ray Images.” 2008 Ieee International Conference on Image Processing, Proceedings, IEEE, 2008, pp. 633-+.
    Link to Item
  - lo, J. Y. “TU‐D‐342‐02: What Every Medical Physicist Should Know About Breast Tomosynthesis.” Medical Physics, vol. 35, no. 6, 2008. Scopus, doi:10.1118/1.2962602.
    Full Text
  - Jerebko, A., et al. “Feasibility study of breast tomosynthesis CAD system.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6514, no. PART 1, 2007. Scopus, doi:10.1117/12.712729.
    Full Text
  - Saunders, R. S., et al. “Initial human subject results for breast Bi-plane correlation imaging technique.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6514, no. PART 2, 2007. Scopus, doi:10.1117/12.713722.
    Full Text
  - Chen, Y., et al. “Methodology of NEQ (f) analysis for optimization and comparison of digital breast tomosynthesis acquisition techniques and reconstruction algorithms.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6510, no. PART 1, 2007. Scopus, doi:10.1117/12.713737.
    Full Text
  - Johnson, J. P., et al. “Visual image quality metrics for optimization of breast tomosynthesis acquisition technique.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6515, 2007. Scopus, doi:10.1117/12.712343.
    Full Text
  - Xia, J. Q., et al. “On the development of a Gaussian noise model for scatter compensation.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 6510, no. PART 2, 2007. Scopus, doi:10.1117/12.712515.
    Full Text
  - Williams, M., et al. “TU‐B‐M100J‐01: Optimizing Mammography Image Quality and Dose: X‐Ray Spectrum and Exposure Parameter Selection.” Medical Physics, vol. 34, no. 6, 2007, pp. 2540–41. Scopus, doi:10.1118/1.2761315.
    Full Text
  - lo, J. Y., et al. “MO‐D‐L100F‐03: New Developments in Digital Breast Tomosynthesis.” Medical Physics, vol. 34, no. 6, 2007. Scopus, doi:10.1118/1.2761222.
    Full Text
  - Chen, Y., et al. “SU‐FF‐I‐21: Two‐Dimensional Shift‐And‐Add (SAA) Algorithm for Digital Breast Tomosynthesis Reconstruction.” Medical Physics, vol. 33, no. 6, 2006. Scopus, doi:10.1118/1.2240260.
    Full Text
  - Hong, A. S., et al. “Computer-aided classification of breast masses using mammogram, ultrasound, and clinical inputs.” American Journal of Roentgenology, vol. 182, no. 4, AMER ROENTGEN RAY SOC, 2004, pp. 33–33.
    Link to Item
  - Baydush, A. H., et al. “Computer-aided detection of lung nodules in chest radiographs using sub-region Hotelling observers.” Radiology, vol. 221, 2001, pp. 548–548.
    Link to Item
  - Land, W. H., et al. “New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data.” Smcia 2001 Proceedings of the 2001 Ieee Mountain Workshop on Soft Computing in Industrial Applications, 2001, pp. 47–52. Scopus, doi:10.1109/SMCIA.2001.936727.
    Full Text
  - LO, J. Y., et al. “COMPUTER-AIDED DIAGNOSIS OF BREAST MASS MALIGNANCY WITH AUTOMATED FEATURE-EXTRACTION AND ARTIFICIAL NEURAL NETWORKS.” Radiology, vol. 197, 1995, pp. 425–425.
    Link to Item
  - FLOYD, C. E., et al. “PREDICTION OF BREAST-CANCER MALIGNANCY FOR DIFFICULT CASES USING AN ARTIFICIAL NEURAL-NETWORK.” World Congress on Neural Networks San Diego 1994 International Neural Network Society Annual Meeting, Vol 1, LAWRENCE ERLBAUM ASSOC PUBL, 1994, pp. A127–32.
    Link to Item
  - Floyd, C. E., et al. “Artificial neural networks for SPECT image reconstruction with optimized weighted backprojection.” Conference Record of the 1991 Ieee Nuclear Science Symposium and Medical Imaging Conference, IEEE. Crossref, doi:10.1109/nssmic.1991.259306.
    Full Text
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