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
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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
- Breast Cancer Detection Consortium awarded by National Institutes of Health 2016 - 2021
- Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS awarded by Department of Defense 2014 - 2020
- Machine learning and collaborative filtering tools for personalized education in digital breast tomosynthesis awarded by National Institutes of Health 2016 - 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
- 3D Printing of Anatomically Realistic Phantoms for Optimization of Imaging Algorithms awarded by National Institutes of Health 2018 - 2020
- Training in Medical Imaging awarded by National Institutes of Health 2003 - 2019
- (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
Industry 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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