Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of Louisville 2008
• 

  Duke Appointment History

  • Associate Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2018 - 2019
  • Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2015 - 2018
  • Associate Professor in Radiology, Radiology, Clinical Science Departments 2017 - 2018
  • Associate Professor in Radiology, Radiology, Clinical Science Departments 2017
  • Assistant Professor of Radiology, Radiology, Clinical Science Departments 2012 - 2017
• Recognition
• 

  In the News

  • NOV 25, 2019

    Innovation and Entrepreneurship

    Incubation Awards to Assist Seven Innovative Research Projects Move Toward the Market
• Research
• 

  Selected Grants

  • Breast Cancer Detection Consortium awarded by National Institutes of Health 2016 - 2022
  • Machine Learning for Risk-Adjusted Breast MRI Screening awarded by City College of New York 2020 - 2021
  • Artificial Intelligence for automatic assessment of TI-RADS features awarded by Radiology Partners Research Institute 2020 - 2021
  • Deep learning for prognostication in breast cancer awarded by North Carolina Biotechnology Center 2018 - 2021
  • Machine learning and collaborative filtering tools for personalized education in digital breast tomosynthesis awarded by National Institutes of Health 2016 - 2021
  • Can contrast dynamics in breast MRI predict genomic intra-tumor heterogeneity awarded by Bracco Diagnostics, Inc. 2016 - 2020
  • Automated Image Quality Assessment Utilizing Machine Learning in Clinical Chest Projection Imaging in Young Children awarded by Society for Pediatric Radiology 2018 - 2019
  • Comparison of cervical transforaminal epidural corticosteroid injections with lateralized interlaminar epidural corticosteroid injections for treatment of cervicogenic upper extremity radiculopathy awarded by American Society of Neuroradiology 2017 - 2019
  • Commericialization of a computational imaging-based biomarker for prognostication in breast cancer awarded by North Carolina Biotechnology Center 2016 - 2017
  • Development of a personalized evidence-based algorithm for the management of suspicious calcifications awarded by Ge-Aur Radiology Research 2015 - 2017
  • Improved education in digital breast tomosynthesis using machine learning and computer vision tools awarded by Radiological Society of North America 2014 - 2016
  • 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
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Modanwal, Gourav, Adithya Vellal, and Maciej A. Mazurowski. “Normalization of breast MRIs using cycle-consistent generative adversarial networks.” Computer Methods and Programs in Biomedicine 208 (September 2021): 106225. https://doi.org/10.1016/j.cmpb.2021.106225.
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    • Swiecicki, Albert, Nianyi Li, Jonathan O’Donnell, Nicholas Said, Jichen Yang, Richard C. Mather, William A. Jiranek, and Maciej A. Mazurowski. “Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists.” Computers in Biology and Medicine 133 (June 2021): 104334. https://doi.org/10.1016/j.compbiomed.2021.104334.
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    • Swiecicki, Albert, Nicholas Konz, Mateusz Buda, and Maciej A. Mazurowski. “A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.” Sci Rep 11, no. 1 (May 13, 2021): 10276. https://doi.org/10.1038/s41598-021-89626-1.
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    • Draelos, Rachel Lea, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, and Lawrence Carin. “Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.” Med Image Anal 67 (January 2021): 101857. https://doi.org/10.1016/j.media.2020.101857.
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    • Devalapalli, Amrita, Samantha Thomas, Maciej A. Mazurowski, Ashirbani Saha, and Lars J. Grimm. “Performance of preoperative breast MRI based on breast cancer molecular subtype.” Clin Imaging 67 (November 2020): 130–35. https://doi.org/10.1016/j.clinimag.2020.05.017.
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    • Wildman-Tobriner, Benjamin, Salmaan Ahmed, Al Erkanli, Maciej A. Mazurowski, and Jenny K. Hoang. “Using the American College of Radiology Thyroid Imaging Reporting and Data System at the Point of Care: Sonographer Performance and Interobserver Variability.” Ultrasound Med Biol 46, no. 8 (August 2020): 1928–33. https://doi.org/10.1016/j.ultrasmedbio.2020.04.019.
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    • Hou, Rui, Maciej A. Mazurowski, Lars J. Grimm, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, Eun-Sil Shelley Hwang, and Joseph Y. Lo. “Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.” Ieee Trans Biomed Eng 67, no. 6 (June 2020): 1565–72. https://doi.org/10.1109/TBME.2019.2940195.
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    • Buda, Mateusz, Benjamin Wildman-Tobriner, Kerry Castor, Jenny K. Hoang, and Maciej A. Mazurowski. “Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images.” Ultrasound Med Biol 46, no. 2 (February 2020): 415–21. https://doi.org/10.1016/j.ultrasmedbio.2019.10.003.
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    • Grimm, Lars J., and Maciej A. Mazurowski. “Breast Cancer Radiogenomics: Current Status and Future Directions.” Acad Radiol 27, no. 1 (January 2020): 39–46. https://doi.org/10.1016/j.acra.2019.09.012.
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    • Mazurowski, Maciej A. “Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers.” Acad Radiol 27, no. 1 (January 2020): 127–29. https://doi.org/10.1016/j.acra.2019.04.024.
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    • Zhu, Zhe, Michael Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, E Shelley Hwang, and Maciej A. Mazurowski. “Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.” Comput Biol Med 115 (December 2019): 103498. https://doi.org/10.1016/j.compbiomed.2019.103498.
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    • Buda, Mateusz, Benjamin Wildman-Tobriner, Jenny K. Hoang, David Thayer, Franklin N. Tessler, William D. Middleton, and Maciej A. Mazurowski. “Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.” Radiology 292, no. 3 (September 2019): 695–701. https://doi.org/10.1148/radiol.2019181343.
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    • Mazurowski, Maciej A. “Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce.” J Am Coll Radiol 16, no. 8 (August 2019): 1077–82. https://doi.org/10.1016/j.jacr.2019.01.026.
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    • Saha, Ashirbani, Lars J. Grimm, Sujata V. Ghate, Connie E. Kim, Mary S. Soo, Sora C. Yoon, and Maciej A. Mazurowski. “Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.” J Magn Reson Imaging 50, no. 2 (August 2019): 456–64. https://doi.org/10.1002/jmri.26636.
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    • Wildman-Tobriner, Benjamin, Mateusz Buda, Jenny K. Hoang, William D. Middleton, David Thayer, Ryan G. Short, Franklin N. Tessler, and Maciej A. Mazurowski. “Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility.” Radiology 292, no. 1 (July 2019): 112–19. https://doi.org/10.1148/radiol.2019182128.
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    • Buda, Mateusz, Ashirbani Saha, and Maciej A. Mazurowski. “Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.” Comput Biol Med 109 (June 2019): 218–25. https://doi.org/10.1016/j.compbiomed.2019.05.002.
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    • Mazurowski, Maciej A., Ashirbani Saha, Michael R. Harowicz, Elizabeth Hope Cain, Jeffrey R. Marks, and P Kelly Marcom. “Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.” J Magn Reson Imaging 49, no. 7 (June 2019): e231–40. https://doi.org/10.1002/jmri.26648.
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    • Zhu, Zhe, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, and Maciej A. Mazurowski. “Deep learning for identifying radiogenomic associations in breast cancer.” Comput Biol Med 109 (June 2019): 85–90. https://doi.org/10.1016/j.compbiomed.2019.04.018.
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    • Euler, André, Justin Solomon, Maciej A. Mazurowski, Ehsan Samei, and Rendon C. Nelson. “How accurate and precise are CT based measurements of iodine concentration? A comparison of the minimum detectable concentration difference among single source and dual source dual energy CT in a phantom study.” Eur Radiol 29, no. 4 (April 2019): 2069–78. https://doi.org/10.1007/s00330-018-5736-0.
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    • Mazurowski, Maciej A., Mateusz Buda, Ashirbani Saha, and Mustafa R. Bashir. “Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.” J Magn Reson Imaging 49, no. 4 (April 2019): 939–54. https://doi.org/10.1002/jmri.26534.
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    • Ho, Lisa M., Ehsan Samei, Maciej A. Mazurowski, Yuese Zheng, Brian C. Allen, Rendon C. Nelson, and Daniele Marin. “Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?” Ajr Am J Roentgenol 212, no. 3 (March 2019): 554–61. https://doi.org/10.2214/AJR.18.20097.
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    • Zhang, Jun, Ashirbani Saha, Zhe Zhu, and Maciej A. Mazurowski. “Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.” Ieee Trans Med Imaging 38, no. 2 (February 2019): 435–47. https://doi.org/10.1109/TMI.2018.2865671.
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    • Cain, Elizabeth Hope, Ashirbani Saha, Michael R. Harowicz, Jeffrey R. Marks, P Kelly Marcom, and Maciej A. Mazurowski. “Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.” Breast Cancer Res Treat 173, no. 2 (January 2019): 455–63. https://doi.org/10.1007/s10549-018-4990-9.
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    • Grimm, Lars J., Ashirbani Saha, Sujata V. Ghate, Connie Kim, Mary Scott Soo, Sora C. Yoon, and Maciej A. Mazurowski. “Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk.” Acad Radiol 26, no. 1 (January 2019): 69–75. https://doi.org/10.1016/j.acra.2018.03.013.
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    • Hou, R., Y. Ren, L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. King, C. C. Maley, E. Shelley Hwang, and J. Y. Lo. “Malignant microcalcification clusters detection using unsupervised deep autoencoders.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 10950 (January 1, 2019). https://doi.org/10.1117/12.2512829.
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    • Saha, Ashirbani, Michael R. Harowicz, Elizabeth Hope Cain, Allison H. Hall, Eun-Sil Shelley Hwang, Jeffrey R. Marks, Paul Kelly Marcom, and Maciej A. Mazurowski. “Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival.” Breast Cancer Res Treat 172, no. 1 (November 2018): 123–32. https://doi.org/10.1007/s10549-018-4879-7.
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    • Buda, Mateusz, Atsuto Maki, and Maciej A. Mazurowski. “A systematic study of the class imbalance problem in convolutional neural networks.” Neural Netw 106 (October 2018): 249–59. https://doi.org/10.1016/j.neunet.2018.07.011.
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    • Wollin, Daniel A., Rajan T. Gupta, Brian Young, Eugene Cone, Adam Kaplan, Daniele Marin, Bhavik N. Patel, et al. “Abdominal Radiography With Digital Tomosynthesis: An Alternative to Computed Tomography for Identification of Urinary Calculi?” Urology 120 (October 2018): 56–61. https://doi.org/10.1016/j.urology.2018.06.041.
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    • Enslow, Michael S., Stephen R. Preece, Benjamin Wildman-Tobriner, Ryan A. Enslow, Maciej Mazurowski, and Rendon C. Nelson. “Splenic contraction: a new member of the hypovolemic shock complex.” Abdom Radiol (Ny) 43, no. 9 (September 2018): 2375–83. https://doi.org/10.1007/s00261-018-1478-3.
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    • Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, Connie E. Kim, Sujata V. Ghate, Ruth Walsh, and Maciej A. Mazurowski. “A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.” Br J Cancer 119, no. 4 (August 2018): 508–16. https://doi.org/10.1038/s41416-018-0185-8.
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    • Saha, Ashirbani, Michael R. Harowicz, and Maciej A. Mazurowski. “Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.” Med Phys 45, no. 7 (July 2018): 3076–85. https://doi.org/10.1002/mp.12925.
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    • Allen, Brian C., Lisa M. Ho, Tracy A. Jaffe, Chad M. Miller, Maciej A. Mazurowski, and Mustafa R. Bashir. “Comparison of Visualization Rates of LI-RADS Version 2014 Major Features With IV Gadobenate Dimeglumine or Gadoxetate Disodium in Patients at Risk for Hepatocellular Carcinoma.” Ajr Am J Roentgenol 210, no. 6 (June 2018): 1266–72. https://doi.org/10.2214/AJR.17.18981.
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    • Saha, Ashirbani, Michael R. Harowicz, Weiyao Wang, and Maciej A. Mazurowski. “A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.” J Cancer Res Clin Oncol 144, no. 5 (May 2018): 799–807. https://doi.org/10.1007/s00432-018-2595-7.
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    • AlBadawy, Ehab A., Ashirbani Saha, and Maciej A. Mazurowski. “Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.” Med Phys 45, no. 3 (March 2018): 1150–58. https://doi.org/10.1002/mp.12752.
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    • Shi, Bibo, Lars J. Grimm, Maciej A. Mazurowski, Jay A. Baker, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, E Shelley Hwang, and Joseph Y. Lo. “Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.” J Am Coll Radiol 15, no. 3 Pt B (March 2018): 527–34. https://doi.org/10.1016/j.jacr.2017.11.036.
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    • Saha, Ashirbani, Xiaozhi Yu, Dushyant Sahoo, and Maciej A. Mazurowski. “Effects of MRI scanner parameters on breast cancer radiomics.” Expert Syst Appl 87 (November 30, 2017): 384–91. https://doi.org/10.1016/j.eswa.2017.06.029.
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    • Harowicz, Michael R., Ashirbani Saha, Lars J. Grimm, P Kelly Marcom, Jeffrey R. Marks, E Shelley Hwang, and Maciej A. Mazurowski. “Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?” J Magn Reson Imaging 46, no. 5 (November 2017): 1332–40. https://doi.org/10.1002/jmri.25655.
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    • Grimm, Lars J., Jing Zhang, Jay A. Baker, Mary S. Soo, Karen S. Johnson, and Maciej A. Mazurowski. “Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype.” Breast J 23, no. 5 (September 2017): 579–82. https://doi.org/10.1111/tbj.12799.
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    • Shi, Bibo, Lars J. Grimm, Maciej A. Mazurowski, Jay A. Baker, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, E Shelley Hwang, and Joseph Y. Lo. “Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?” Acad Radiol 24, no. 9 (September 2017): 1139–47. https://doi.org/10.1016/j.acra.2017.03.013.
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    • Wildman-Tobriner, Benjamin, Brian C. Allen, Mustafa R. Bashir, Morgan Camp, Chad Miller, Lauren E. Fiorillo, Alan Cubre, et al. “Structured reporting of CT enterography for inflammatory bowel disease: effect on key feature reporting, accuracy across training levels, and subjective assessment of disease by referring physicians.” Abdom Radiol (Ny) 42, no. 9 (September 2017): 2243–50. https://doi.org/10.1007/s00261-017-1136-1.
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    • Mazurowski, Maciej A., Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, and Ashirbani Saha. “Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.” J Neurooncol 133, no. 1 (May 2017): 27–35. https://doi.org/10.1007/s11060-017-2420-1.
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    • Czarnek, Nicholas, Kal Clark, Katherine B. Peters, and Maciej A. Mazurowski. “Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study.” J Neurooncol 132, no. 1 (March 2017): 55–62. https://doi.org/10.1007/s11060-016-2359-7.
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    • Tanpitukpongse, T. P., M. A. Mazurowski, J. Ikhena, J. R. Petrella, and J. R. Alzheimer’s Disease Neuroimaging Initiative. “Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?” Ajnr Am J Neuroradiol 38, no. 3 (March 2017): 546–52. https://doi.org/10.3174/ajnr.A5061.
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    • Harowicz, Michael R., Timothy J. Robinson, Michaela A. Dinan, Ashirbani Saha, Jeffrey R. Marks, P Kelly Marcom, and Maciej A. Mazurowski. “Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset.” Breast Cancer Res Treat 162, no. 1 (February 2017): 1–10. https://doi.org/10.1007/s10549-016-4093-4.
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    • Wang, M., L. J. Grimm, and M. A. Mazurowski. “A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases.” Expert Systems With Applications 64 (December 1, 2016): 490–99. https://doi.org/10.1016/j.eswa.2016.08.023.
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    • Wang, M., J. Zhang, L. J. Grimm, S. V. Ghate, R. Walsh, K. S. Johnson, J. Y. Lo, and M. A. Mazurowski. “Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.” Expert Systems With Applications 56 (September 1, 2016): 1–8. https://doi.org/10.1016/j.eswa.2016.01.053.
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    • Saha, Ashirbani, Lars J. Grimm, Michael Harowicz, Sujata V. Ghate, Connie Kim, Ruth Walsh, and Maciej A. Mazurowski. “Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.” Med Phys 43, no. 8 (August 2016): 4558. https://doi.org/10.1118/1.4955435.
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    • Grimm, Lars J., Jing Zhang, Joseph Y. Lo, Karen S. Johnson, Sujata V. Ghate, Ruth Walsh, and Maciej A. Mazurowski. “Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.” J Am Coll Radiol 13, no. 2 (February 2016): 198–202. https://doi.org/10.1016/j.jacr.2015.09.025.
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    • Mazurowski, Maciej A. “Author's Reply.” J Am Coll Radiol 13, no. 2 (February 2016): 121–22. https://doi.org/10.1016/j.jacr.2015.11.017.
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    • Mazurowski, Maciej A., Lars J. Grimm, Jing Zhang, P Kelly Marcom, Sora C. Yoon, Connie Kim, Sujata V. Ghate, and Karen S. Johnson. “Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.” Eur J Radiol 84, no. 11 (November 2015): 2117–22. https://doi.org/10.1016/j.ejrad.2015.07.012.
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    • Grimm, Lars J., Jing Zhang, and Maciej A. Mazurowski. “Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.” J Magn Reson Imaging 42, no. 4 (October 2015): 902–7. https://doi.org/10.1002/jmri.24879.
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    • Mazurowski, Maciej A. “Radiogenomics: what it is and why it is important.” J Am Coll Radiol 12, no. 8 (August 2015): 862–66. https://doi.org/10.1016/j.jacr.2015.04.019.
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    • Zhang, Jing, Lars J. Grimm, Joseph Y. Lo, Karen S. Johnson, Sujata V. Ghate, Ruth Walsh, and Maciej A. Mazurowski. “Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis?” J Am Coll Radiol 12, no. 7 (July 2015): 728–32. https://doi.org/10.1016/j.jacr.2015.02.025.
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    • Zhang, Jing, James I. Silber, and Maciej A. Mazurowski. “Modeling false positive error making patterns in radiology trainees for improved mammography education.” J Biomed Inform 54 (April 2015): 50–57. https://doi.org/10.1016/j.jbi.2015.01.007.
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    • Mazurowski, Maciej A., Jing Zhang, Katherine B. Peters, and Hasan Hobbs. “Computer-extracted MR imaging features are associated with survival in glioblastoma patients.” J Neurooncol 120, no. 3 (December 2014): 483–88. https://doi.org/10.1007/s11060-014-1580-5.
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    • Mazurowski, Maciej A., Jing Zhang, Lars J. Grimm, Sora C. Yoon, and James I. Silber. “Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.” Radiology 273, no. 2 (November 2014): 365–72. https://doi.org/10.1148/radiol.14132641.
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    • Zhang, Jing, Joseph Y. Lo, Cherie M. Kuzmiak, Sujata V. Ghate, Sora C. Yoon, and Maciej A. Mazurowski. “Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.” Med Phys 41, no. 9 (September 2014): 091907. https://doi.org/10.1118/1.4892173.
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    • Grimm, Lars J., Cherie M. Kuzmiak, Sujata V. Ghate, Sora C. Yoon, and Maciej A. Mazurowski. “Radiology resident mammography training: interpretation difficulty and error-making patterns.” Acad Radiol 21, no. 7 (July 2014): 888–92. https://doi.org/10.1016/j.acra.2014.01.025.
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    • Grimm, Lars J., Lauren M. Shapiro, Terry Singhapricha, Maciej A. Mazurowski, Terry S. Desser, and Charles M. Maxfield. “Predictors of an academic career on radiology residency applications.” Acad Radiol 21, no. 5 (May 2014): 685–90. https://doi.org/10.1016/j.acra.2013.10.019.
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    • Zhang, Jing, Daniel P. Barboriak, Hasan Hobbs, and Maciej A. Mazurowski. “A fully automatic extraction of magnetic resonance image features in glioblastoma patients.” Med Phys 41, no. 4 (April 2014): 042301. https://doi.org/10.1118/1.4866218.
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    • Grimm, Lars J., Sujata V. Ghate, Sora C. Yoon, Cherie M. Kuzmiak, Connie Kim, and Maciej A. Mazurowski. “Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.” Med Phys 41, no. 3 (March 2014): 031909. https://doi.org/10.1118/1.4866379.
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    • Mazurowski, Maciej Andrzej, Annick Desjardins, and Jordan Milton Malof. “Imaging descriptors improve the predictive power of survival models for glioblastoma patients.” Neuro Oncol 15, no. 10 (October 2013): 1389–94. https://doi.org/10.1093/neuonc/nos335.
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    • Mazurowski, M. A. “Estimating confidence of individual rating predictions in collaborative filtering recommender systems.” Expert Systems With Applications 40, no. 10 (August 1, 2013): 3847–57. https://doi.org/10.1016/j.eswa.2012.12.102.
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    • Mazurowski, M. A. “Difficulty of mammographic cases in the context of resident training: Preliminary experimental data.” Proceedings of Spie  the International Society for Optical Engineering 8673 (June 14, 2013). https://doi.org/10.1117/12.2008550.
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    • Mazurowski, Maciej A., Huiman X. Barnhart, Jay A. Baker, and Georgia D. Tourassi. “Identifying error-making patterns in assessment of mammographic BI-RADS descriptors among radiology residents using statistical pattern recognition.” Acad Radiol 19, no. 7 (July 2012): 865–71. https://doi.org/10.1016/j.acra.2012.01.012.
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    • Malof, Jordan M., Maciej A. Mazurowski, and Georgia D. Tourassi. “The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.” Neural Netw 25, no. 1 (January 2012): 141–45. https://doi.org/10.1016/j.neunet.2011.07.002.
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    • Mazurowski, Maciej A., Joseph Y. Lo, Brian P. Harrawood, and Georgia D. Tourassi. “Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis.” J Biomed Inform 44, no. 5 (October 2011): 815–23. https://doi.org/10.1016/j.jbi.2011.04.008.
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    • Mazurowski, M. A., and G. D. Tourassi. “Exploring the potential of collaborative filtering for user-adaptive mammography education.” Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, Bsec 2011, July 7, 2011. https://doi.org/10.1109/BSEC.2011.5872325.
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    • Mazurowski, M. A., and G. D. Tourassi. “Modeling error in assessment of mammographic image features for improved computer-aided mammography training: Initial experience.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 7966 (May 16, 2011). https://doi.org/10.1117/12.878737.
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    • Mazurowski, Maciej A., Jordan M. Malof, and Georgia D. Tourassi. “Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support.” Phys Med Biol 56, no. 2 (January 21, 2011): 473–89. https://doi.org/10.1088/0031-9155/56/2/012.
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    • Tourassi, Georgia D., Maciej A. Mazurowski, Brian P. Harrawood, and Elizabeth A. Krupinski. “Exploring the potential of context-sensitive CADe in screening mammography.” Med Phys 37, no. 11 (November 2010): 5728–36. https://doi.org/10.1118/1.3501882.
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    • Mazurowski, Maciej A., Jay A. Baker, Huiman X. Barnhart, and Georgia D. Tourassi. “Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments.” Med Phys 37, no. 3 (March 2010): 1152–60. https://doi.org/10.1118/1.3301575.
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    • Malof, J. M., M. A. Mazurowski, and G. D. Tourassi. “The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems.” Proceedings of the International Joint Conference on Neural Networks, November 18, 2009, 1975–80. https://doi.org/10.1109/IJCNN.2009.5178759.
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    • Mazurowski, M. A., and G. D. Tourassi. “Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy.” Proceedings of the International Joint Conference on Neural Networks, November 18, 2009, 2045–49. https://doi.org/10.1109/IJCNN.2009.5178752.
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    • Zurada, J. M., M. A. Mazurowski, R. Ragade, A. Abdullin, J. Wojtudiak, and J. Gentle. “Building virtual community in computational intelligence and machine learning.” Ieee Computational Intelligence Magazine 4, no. 1 (November 6, 2009). https://doi.org/10.1109/MCI.2008.930986.
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    • Mazurowski, Maciej A., Jacek M. Zurada, and Georgia D. Tourassi. “An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.” Med Phys 36, no. 7 (July 2009): 2976–84. https://doi.org/10.1118/1.3132304.
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    • Mazurowski, M. A., J. M. Malof, J. M. Zurada, and G. D. Tourassi. “A comparative study of database reduction methods for case-based computer-aided detection systems: Preliminary results.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 7260 (June 15, 2009). https://doi.org/10.1117/12.812442.
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    • Mazurowski, M. A., and G. D. Tourassi. “Relational representation for improved decisions with an information-theoretic CADe system: Initial experience.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 7260 (June 15, 2009). https://doi.org/10.1117/12.812965.
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    • Zurada, J. M., I. Aizenberg, and M. A. Mazurowski. “Learning in networks: Complex-valued neurons, pruning, and rule extraction.” 2008 4th International Ieee Conference Intelligent Systems, Is 2008 1 (December 1, 2008): 115–20. https://doi.org/10.1109/IS.2008.4670394.
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    • Zurada, J. M., J. Wojtusiak, F. Chowdhury, J. E. Gentle, C. J. Jeannot, and M. A. Mazurowski. “Computational intelligence virtual community: Framework and implementation issues.” Proceedings of the International Joint Conference on Neural Networks, November 24, 2008, 3153–57. https://doi.org/10.1109/IJCNN.2008.4634244.
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    • Mazurowski, Maciej A., Jacek M. Zurada, and Georgia D. Tourassi. “Selection of examples in case-based computer-aided decision systems.” Phys Med Biol 53, no. 21 (November 7, 2008): 6079–96. https://doi.org/10.1088/0031-9155/53/21/013.
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    • Mazurowski, M. A., J. M. Zurada, and G. D. Tourassi. “Reliability assessment of ensemble classifiers: Application in mammography.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5116 LNCS (September 9, 2008): 366–70. https://doi.org/10.1007/978-3-540-70538-3_51.
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    • Mazurowski, M. A., B. P. Harrawood, J. M. Zurada, and G. D. Tourassi. “Toward perceptually driven image retrieval in mammography: A pilot observer study to assess visual similarity of masses.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 6917 (June 18, 2008). https://doi.org/10.1117/12.772125.
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    • Mazurowski, M. A., J. M. Zurada, and G. D. Tourassi. “Database decomposition of a knowledge-based CAD system in mammography; An ensemble approach to improve detection.” Progress in Biomedical Optics and Imaging  Proceedings of Spie 6915 (June 2, 2008). https://doi.org/10.1117/12.771556.
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    • Mazurowski, Maciej A., Piotr A. Habas, Jacek M. Zurada, Joseph Y. Lo, Jay A. Baker, and Georgia D. Tourassi. “Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.” Neural Netw 21, no. 2–3 (March 2008): 427–36. https://doi.org/10.1016/j.neunet.2007.12.031.
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    • Mazurowski, Maciej A., Piotr A. Habas, Jacek M. Zurada, and Georgia D. Tourassi. “Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.” Phys Med Biol 53, no. 4 (February 21, 2008): 895–908. https://doi.org/10.1088/0031-9155/53/4/005.
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    • Mazurowski, M. A., P. A. Habas, J. M. Zurada, and G. D. Tourassi. “Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms.” 2007 Ieee Congress on Evolutionary Computation, Cec 2007, December 1, 2007, 600–605. https://doi.org/10.1109/CEC.2007.4424525.
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    • Mazurowski, M. A., P. A. Habas, J. M. Zurada, and G. D. Tourassi. “Impact of low class prevalence on the performance evaluation of neural network based classifiers: Experimental study in the context of computer-assisted medical diagnosis.” Ieee International Conference on Neural Networks  Conference Proceedings, December 1, 2007, 2005–9. https://doi.org/10.1109/IJCNN.2007.4371266.
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    • Mazurowski, M. A., and J. M. Zurada. “Solving decentralized multi-agent control problems with genetic algorithms.” 2007 Ieee Congress on Evolutionary Computation, Cec 2007, December 1, 2007, 1029–34. https://doi.org/10.1109/CEC.2007.4424583.
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    • Tourassi, G. D., J. L. Jesneck, M. A. Mazurowski, and P. A. Habas. “Stacked generalization in computer-assisted decision systems: Empirical comparison of data handling schemes.” Ieee International Conference on Neural Networks  Conference Proceedings, December 1, 2007, 1343–47. https://doi.org/10.1109/IJCNN.2007.4371153.
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    • Mazurowski, M. A., and J. M. Zurada. “Solving multi-agent control problems using particle swarm optimization.” Proceedings of the 2007 Ieee Swarm Intelligence Symposium, Sis 2007, September 25, 2007, 105–11. https://doi.org/10.1109/SIS.2007.368033.
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    • Mazurowski, M. A., and J. M. Zurada. “Emergence of communication in multi-agent systems using reinforcement learning.” 2006 Ieee International Conference on Computational Cybernetics, Iccc, December 1, 2006. https://doi.org/10.1109/ICCCYB.2006.305696.
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    • Mazurowski, M. A., and P. M. Szecówka. “Limitations of sensitivity analysis for neural networks in cases with dependent inputs.” 2006 Ieee International Conference on Computational Cybernetics, Iccc, December 1, 2006. https://doi.org/10.1109/ICCCYB.2006.305714.
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    • Szecówka, P. M., A. Szczurek, M. A. Mazurowski, B. W. Licznerski, and F. Pichler. “Neural network sensitivity analysis applied for the reduction of the sensor matrix.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3643 LNCS (January 1, 2005): 27–32. https://doi.org/10.1007/11556985_5.
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  • Book Sections

    • Tourassi, G. D., and M. A. Mazurowski. “Case-based CAD systems in breast imaging.” In Computer-Aided Detection and Diagnosis in Medical Imaging, 95–109, 2015. https://doi.org/10.1201/b18191.
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  • Conference Papers

    • Zhu, Z., M. R. Bashir, and M. A. Mazurowski. “Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11597, 2021. https://doi.org/10.1117/12.2582190.
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    • Hou, R., L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, E. S. Hwang, and J. Y. Lo. “A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2549669.
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    • Li, N., A. Swiecicki, N. Said, J. O’Donnell, W. A. Jiranek, and M. A. Mazurowski. “Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2551392.
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    • Modanwal, G., A. Vellal, M. Buda, and M. A. Mazurowski. “MRI image harmonization using cycle-consistent generative adversarial network.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2551301.
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    • Saha, A., F. I. Tushar, K. Faryna, V. M. D’Anniballe, R. Hou, M. A. Mazurowski, G. D. Rubin, and J. Y. Lo. “Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2550857.
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    • Samei, E., E. Abadi, A. Kapadia, J. Lo, M. Mazurowski, and P. Segars. “Virtual imaging trials: An emerging experimental paradigm in imaging research and practice.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11312, 2020. https://doi.org/10.1117/12.2549818.
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    • Swiecicki, A., N. Said, J. O’Donnell, M. Buda, N. Li, W. A. Jiranek, and M. A. Mazurowski. “Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2551377.
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    • “Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 11314, 2020. https://doi.org/10.1117/12.2551379.
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    • Benitez, M., J. Tian, M. Kelly, V. Selvakumaran, M. Phelan, M. Mazurowski, J. Y. Lo, G. D. Rubin, and R. Henao. “Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10950, 2019. https://doi.org/10.1117/12.2512886.
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    • Liu, Y., W. Fu, V. Selvakumaran, M. Phelan, W. P. Segars, E. Samei, M. Mazurowski, J. Y. Lo, G. D. Rubin, and R. Henao. “Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10954, 2019. https://doi.org/10.1117/12.2512887.
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    • Hou, R., B. Shi, L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, E. Shelley Hwang, and J. Y. Lo. “Improving classification with forced labeling of other related classes: Application to prediction of upstaged ductal carcinoma in situ using mammographic features.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2293809.
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    • Saha, A., M. R. Harowicz, L. J. Grimm, C. E. Kim, S. V. Ghate, R. Walsh, and M. A. Mazurowski. “Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: A large scale evaluation.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2293207.
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    • Shi, B., R. Hou, M. A. Mazurowski, L. J. Grimm, Y. Ren, J. R. Marks, L. M. King, C. C. Maley, E. S. Hwang, and J. Y. Lo. “Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2293594.
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    • Zhang, J., A. Saha, Z. Zhu, and M. A. Mazurowski. “Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295436.
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    • Zhang, J., E. H. Cain, A. Saha, Z. Zhu, and M. A. Mazurowski. “Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295443.
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    • Zhang, J., S. V. Ghate, L. J. Grimm, A. Saha, E. H. Cain, Z. Zhu, and M. A. Mazurowski. “Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295437.
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    • Zhu, Z., E. Albadawy, A. Saha, J. Zhang, M. R. Harowicz, and M. A. Mazurowski. “Breast cancer molecular subtype classification using deep features: Preliminary results.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295471.
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    • Zhu, Z., M. Harowicz, J. Zhang, A. Saha, L. J. Grimm, S. Hwang, and M. A. Mazurowski. “Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: Preliminary data.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10575, 2018. https://doi.org/10.1117/12.2295470.
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    • Harowicz, M. R., J. R. Marks, P. Kelly Marcom, and M. A. Mazurowski. “Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?” In Proceedings of Spie  the International Society for Optical Engineering, Vol. 10327, 2017.
    • Harowicz, M. R., J. R. Marks, P. Kelly Marcom, and M. A. Mazurowski. “Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10136, 2017. https://doi.org/10.1117/12.2255866.
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    • Mazurowski, M. A., K. Clark, N. M. Czarnek, P. Shamsesfandabadi, K. B. Peters, and A. Saha. “Radiogenomic analysis of lower grade glioma: A pilot multi-institutional study shows an association between quantitative image features and tumor genomics.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10134, 2017. https://doi.org/10.1117/12.2255579.
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    • Paredes, D., A. Saha, and M. A. Mazurowski. “Deep learning for segmentation of brain tumors: Can we train with images from different institutions?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10134, 2017. https://doi.org/10.1117/12.2255696.
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    • Ren, B., and M. A. Mazurowski. “Statistical aspects of radiogenomics: Can radiogenomics models be used to aid prediction of outcomes in cancer patients?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10136, 2017. https://doi.org/10.1117/12.2255898.
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    • Shi, B., L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, E. S. Hwang, and J. Y. Lo. “Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10134, 2017. https://doi.org/10.1117/12.2255731.
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    • Shi, B., L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, E. S. Hwang, and J. Y. Lo. “Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 10134, 2017. https://doi.org/10.1117/12.2255847.
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    • Czarnek, N. M., K. Clark, K. B. Peters, L. M. Collins, and M. A. Mazurowski. “Radiogenomics of glioblastoma: A pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9785, 2016. https://doi.org/10.1117/12.2217084.
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    • Mazurowski, M. A., N. M. Czarnek, L. M. Collins, K. B. Peters, and K. Clark. “Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape - Preliminary data.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9785, 2016. https://doi.org/10.1117/12.2217098.
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    • Wang, M., J. Zhang, L. J. Grimm, S. V. Ghate, R. Walsh, K. S. Johnson, J. Y. Lo, and M. A. Mazurowski. “Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9787, 2016. https://doi.org/10.1117/12.2201061.
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    • Grimm, L. J., J. Zhang, K. S. Johnson, J. Y. Lo, and M. A. Mazurowski. “Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality?” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9416, 2015. https://doi.org/10.1117/12.2082810.
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    • Mazurowski, M. A., J. Zhang, J. Y. Lo, C. M. Kuzmiak, S. V. Ghate, and S. Yoon. “Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9037, 2014. https://doi.org/10.1117/12.2044404.
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    • Mazurowski, M. A., J. Y. Lo, and G. D. Tourassi. “User modeling for improved computer-aided training in radiology: Initial experience.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 7627, 2010. https://doi.org/10.1117/12.843863.
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    • Tourassi, G. D., M. A. Mazurowski, and E. A. Krupinski. “Perception-driven IT-CADe analysis for the detection of masses in screening mammography: Initial investigation.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 7624, 2010. https://doi.org/10.1117/12.845495.
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• Teaching & Mentoring
• 

  Recent Courses

  • ECE 899: Special Readings in Electrical Engineering 2020
  • MEDPHY 762: Data Science 2020
  • BIOSTAT 824: Case Studies in Biomedical Data Science 2019
  • ECE 493: Projects in Electrical and Computer Engineering 2019
  • MEDPHY 762: Data Science 2019

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