Maciej A Mazurowski

Biostatistics & Bioinformatics, Division of Translational Biomedical

Associate Professor of Biostatistics & Bioinformatics

Maciej A Mazurowski Profile Photo

Education

Ph.D. University of Louisville, 2008

Positions

Associate Professor of Biostatistics & Bioinformatics
Associate Professor in Radiology
Associate Professor of Computer Science
Associate Professor in the Department of Electrical and Computer Engineering
Member of the Duke Cancer Institute

Courses Taught

ECE 899: Special Readings in Electrical Engineering
COMPSCI 393: Research Independent Study

Publications

Miller CM, Zhu Z, Mazurowski MA, Bashir MR, Wiggins WF. Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier. Abdom Radiol (NY). 2024 Oct;49(10):3735–46.
Lew CO, Harouni M, Kirksey ER, Kang EJ, Dong H, Gu H, et al. A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI. Sci Rep. 2024 Mar 5;14(1):5383.
Prabhu NK, Wong MK, Klapper JA, Haney JC, Mazurowski MA, Mammarappallil JG, et al. Computed Tomography Volumetrics for Size Matching in Lung Transplantation for Restrictive Disease. Ann Thorac Surg. 2024 Feb;117(2):413–21.
Zhang Y, Mazurowski MA. Convolutional neural networks rarely learn shape for semantic segmentation. Pattern Recognition. 2024 Feb 1;146.
Wildman-Tobriner B, Yang J, Allen BC, Ho LM, Miller CM, Mazurowski MA. Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system. Curr Probl Diagn Radiol. 2024;53(6):695–9.
Dong H, Konz N, Gu H, Mazurowski MA. Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2024. p. 5046–55.
Konz N, Mazurowski MA. THE EFFECT OF INTRINSIC DATASET PROPERTIES ON GENERALIZATION: UNRAVELING LEARNING DIFFERENCES BETWEEN NATURAL AND MEDICAL IMAGES. In: 12th International Conference on Learning Representations, ICLR 2024. 2024.
Zhang J, Santos C, Park C, Mazurowski MA, Colglazier R. Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach. J Digit Imaging. 2023 Dec;36(6):2402–10.
Dong H, Zhang Y, Gu H, Konz N, Mazurowski MA. SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images. IEEE transactions on medical imaging. 2023 Dec;42(12):3860–70.
Mazurowski MA, Dong H, Gu H, Yang J, Konz N, Zhang Y. Segment anything model for medical image analysis: An experimental study. Med Image Anal. 2023 Oct;89:102918.
Lew CO, Zhou L, Mazurowski MA, Doraiswamy PM, Petrella JR, Alzheimer’s Disease Neuroimaging Initiative. MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum. Radiology. 2023 Oct;309(1):e222441.
Zhang J, Mazurowski MA, Grimm LJ. Feasibility of predicting a screening digital breast tomosynthesis recall using features extracted from the electronic medical record. Eur J Radiol. 2023 Sep;166:110979.
Macdonald JA, Zhu Z, Konkel B, Mazurowski MA, Wiggins WF, Bashir MR. Duke Liver Dataset: A Publicly Available Liver MRI Dataset with Liver Segmentation Masks and Series Labels. Radiol Artif Intell. 2023 Sep;5(5):e220275.
Konz N, Dong H, Mazurowski MA. Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion. Med Image Anal. 2023 Jul;87:102836.
Weng J, Wildman-Tobriner B, Buda M, Yang J, Ho LM, Allen BC, et al. Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset. Clin Imaging. 2023 Jul;99:60–6.
Zhang J, Mazurowski MA, Allen BC, Wildman-Tobriner B. Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. Artif Intell Med. 2023 Jul;141:102553.
Cao S, Konz N, Duncan J, Mazurowski MA. Deep Learning for Breast MRI Style Transfer with Limited Training Data. J Digit Imaging. 2023 Apr;36(2):666–78.
Yang J, Page LC, Wagner L, Wildman-Tobriner B, Bisset L, Frush D, et al. Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm. AJR Am J Roentgenol. 2023 Mar;220(3):408–17.
Konz N, Buda M, Gu H, Saha A, Yang J, Chledowski J, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open. 2023 Feb 1;6(2):e230524.
Gu H, He H, Colglazier R, Axelrod J, French R, Mazurowski MA. SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs. In: Proceedings of Machine Learning Research. 2023. p. 119–33.
Konz N, Mazurowski MA. Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images. In: Proceedings of Machine Learning Research. 2023. p. 829–45.
Goldstein BA, Mazurowski MA, Li C. The Need for Targeted Labeling of Machine Learning-Based Software as a Medical Device. JAMA Netw Open. 2022 Nov 1;5(11):e2242351.
Wildman-Tobriner B, Taghi-Zadeh E, Mazurowski MA. Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications. AJR Am J Roentgenol. 2022 Oct;219(4):1–8.
Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski MA, Marks JR, et al. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE Trans Biomed Eng. 2022 May;69(5):1639–50.
D’Anniballe VM, Tushar FI, Faryna K, Han S, Mazurowski MA, Rubin GD, et al. Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. BMC Med Inform Decis Mak. 2022 Apr 15;22(1):102.
Zhu Z, Mittendorf A, Shropshire E, Allen B, Miller C, Bashir MR, et al. 3D Pyramid Pooling Network for Abdominal MRI Series Classification. IEEE Trans Pattern Anal Mach Intell. 2022 Apr;44(4):1688–98.
Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology. 2022 Apr;303(1):54–62.
Zhang Y, Dong H, Konz N, Gu H, Mazurowski MA. Lightweight Transformer Backbone for Medical Object Detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 47–56.
Tushar FI, D’Anniballe VM, Hou R, Mazurowski MA, Fu W, Samei E, et al. Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell. 2022 Jan;4(1):e210026.
Dahal L, Tushar FI, Abadi E, Fricks RB, Mazurowski M, Segars WP, et al. Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Tushar FI, Nujaim H, Fu W, Abadi E, Mazurowski MA, Segars WP, et al. Quality or quantity: toward a unified approach for multi-organ segmentation in body CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Konz N, Gu H, Dong H, Mazurowski M. The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 684–94.
Tushar FI, Abadi E, Sotoudeh-Paima S, Fricks RB, Mazurowski MA, Segars WP, et al. Virtual vs. reality: External validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Modanwal G, Vellal A, Mazurowski MA. Normalization of breast MRIs using cycle-consistent generative adversarial networks. Comput Methods Programs Biomed. 2021 Sep;208:106225.
Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, et al. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Netw Open. 2021 Aug 2;4(8):e2119100.
Mazurowski MA. Do We Expect More from Radiology AI than from Radiologists? Radiol Artif Intell. 2021 Jul;3(4):e200221.
Swiecicki A, Li N, O’Donnell J, Said N, Yang J, Mather RC, et al. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Comput Biol Med. 2021 Jun;133:104334.
Swiecicki A, Konz N, Buda M, Mazurowski MA. A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis. Sci Rep. 2021 May 13;11(1):10276.
Zhu Z, Bashir MR, Mazurowski MA. Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.
Draelos RL, Dov D, Mazurowski MA, Lo JY, Henao R, Rubin GD, et al. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical image analysis. 2021 Jan;67:101857.
Devalapalli A, Thomas S, Mazurowski MA, Saha A, Grimm LJ. Performance of preoperative breast MRI based on breast cancer molecular subtype. Clin Imaging. 2020 Nov;67:130–5.
Wildman-Tobriner B, Ahmed S, Erkanli A, Mazurowski MA, Hoang JK. 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. 2020 Aug;46(8):1928–33.
Hou R, Mazurowski MA, Grimm LJ, Marks JR, King LM, Maley CC, et al. Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation. IEEE Trans Biomed Eng. 2020 Jun;67(6):1565–72.
Buda M, Wildman-Tobriner B, Castor K, Hoang JK, Mazurowski MA. Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images. Ultrasound Med Biol. 2020 Feb;46(2):415–21.
Samei E, Abadi E, Kapadia A, Lo J, Mazurowski M, Segars P. Virtual imaging trials: An emerging experimental paradigm in imaging research and practice. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Mazurowski MA. Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers. Acad Radiol. 2020 Jan;27(1):127–9.
Swiecicki A, Buda M, Saha A, Li N, Ghate SV, Walsh R, et al. 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. 2020.
Modanwal G, Vellal A, Buda M, Mazurowski MA. MRI image harmonization using cycle-consistent generative adversarial network. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Saha A, Tushar FI, Faryna K, D’Anniballe VM, Hou R, Mazurowski MA, et al. Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Grimm LJ, Mazurowski MA. Breast Cancer Radiogenomics: Current Status and Future Directions. Acad Radiol. 2020 Jan;27(1):39–46.
Swiecicki A, Said N, O’Donnell J, Buda M, Li N, Jiranek WA, et al. Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Li N, Swiecicki A, Said N, O’Donnell J, Jiranek WA, Mazurowski MA. Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Buda M, AlBadawy EA, Saha A, Mazurowski MA. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiol Artif Intell. 2020 Jan;2(1):e180050.
Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. 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. 2020.
Zhu Z, Harowicz M, Zhang J, Saha A, Grimm LJ, Hwang ES, et al. Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. Comput Biol Med. 2019 Dec;115:103498.
Buda M, Wildman-Tobriner B, Hoang JK, Thayer D, Tessler FN, Middleton WD, et al. Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. Radiology. 2019 Sep;292(3):695–701.
Mazurowski MA. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. J Am Coll Radiol. 2019 Aug;16(8):1077–82.
Saha A, Grimm LJ, Ghate SV, Kim CE, Soo MS, Yoon SC, et al. Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI. J Magn Reson Imaging. 2019 Aug;50(2):456–64.
Wildman-Tobriner B, Buda M, Hoang JK, Middleton WD, Thayer D, Short RG, et al. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology. 2019 Jul;292(1):112–9.
Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med. 2019 Jun;109:85–90.
Buda M, Saha A, Mazurowski MA. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput Biol Med. 2019 Jun;109:218–25.
Mazurowski MA, Saha A, Harowicz MR, Cain EH, Marks JR, Marcom PK. Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer. J Magn Reson Imaging. 2019 Jun;49(7):e231–40.
Mazurowski MA, Buda M, Saha A, Bashir MR. 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. 2019 Apr;49(4):939–54.
Euler A, Solomon J, Mazurowski MA, Samei E, Nelson RC. 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. 2019 Apr;29(4):2069–78.
Ho LM, Samei E, Mazurowski MA, Zheng Y, Allen BC, Nelson RC, et al. 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. 2019 Mar;212(3):554–61.
Zhang J, Saha A, Zhu Z, Mazurowski MA. Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics. IEEE Trans Med Imaging. 2019 Feb;38(2):435–47.
Liu Y, Fu W, Selvakumaran V, Phelan M, Segars WP, Samei E, et al. 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. 2019.
Grimm LJ, Saha A, Ghate SV, Kim C, Soo MS, Yoon SC, et al. Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk. Acad Radiol. 2019 Jan;26(1):69–75.
Hou R, Ren Y, Grimm LJ, Mazurowski MA, Marks JR, King L, et al. Malignant microcalcification clusters detection using unsupervised deep autoencoders. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019 Jan 1;10950.
Benitez M, Tian J, Kelly M, Selvakumaran V, Phelan M, Mazurowski M, et al. 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. 2019.
Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. 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. 2019 Jan;173(2):455–63.
Saha A, Harowicz MR, Cain EH, Hall AH, Hwang E-SS, Marks JR, et al. Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival. Breast Cancer Res Treat. 2018 Nov;172(1):123–32.
Buda M, Maki A, Mazurowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 2018 Oct;106:249–59.
Wollin DA, Gupta RT, Young B, Cone E, Kaplan A, Marin D, et al. Abdominal Radiography With Digital Tomosynthesis: An Alternative to Computed Tomography for Identification of Urinary Calculi? Urology. 2018 Oct;120:56–61.
Enslow MS, Preece SR, Wildman-Tobriner B, Enslow RA, Mazurowski M, Nelson RC. Splenic contraction: a new member of the hypovolemic shock complex. Abdom Radiol (NY). 2018 Sep;43(9):2375–83.
Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, et al. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018 Aug;119(4):508–16.
Saha A, Harowicz MR, Mazurowski MA. Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors. Med Phys. 2018 Jul;45(7):3076–85.
Allen BC, Ho LM, Jaffe TA, Miller CM, Mazurowski MA, Bashir MR. 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. 2018 Jun;210(6):1266–72.
Saha A, Harowicz MR, Wang W, Mazurowski MA. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. J Cancer Res Clin Oncol. 2018 May;144(5):799–807.
AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 2018 Mar;45(3):1150–8.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. J Am Coll Radiol. 2018 Mar;15(3 Pt B):527–34.
Zhang J, Ghate SV, Grimm LJ, Saha A, Cain EH, Zhu Z, et al. Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Hou R, Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, et al. 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. 2018.
Auffermann WF, Mazurowski M. Perception and Training. In: The Handbook of Medical Image Perception and Techniques: Second Edition. 2018. p. 470–82.
Shi B, Hou R, Mazurowski MA, Grimm LJ, Ren Y, Marks JR, et al. 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. 2018.
Zhu Z, Harowicz M, Zhang J, Saha A, Grimm LJ, Hwang S, et al. 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. 2018.
Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, et al. 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. 2018.
Zhang J, Cain EH, Saha A, Zhu Z, Mazurowski MA. Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Zhang J, Saha A, Zhu Z, Mazurowski MA. 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. 2018.
Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Breast cancer molecular subtype classification using deep features: Preliminary results. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Saha A, Yu X, Sahoo D, Mazurowski MA. Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl. 2017 Nov 30;87:384–91.
Harowicz MR, Saha A, Grimm LJ, Marcom PK, Marks JR, Hwang ES, et al. 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. 2017 Nov;46(5):1332–40.
Wildman-Tobriner B, Allen BC, Bashir MR, Camp M, Miller C, Fiorillo LE, 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). 2017 Sep;42(9):2243–50.
Grimm LJ, Zhang J, Baker JA, Soo MS, Johnson KS, Mazurowski MA. 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. 2017 Sep;23(5):579–82.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol. 2017 Sep;24(9):1139–47.
Mazurowski MA, Clark K, Czarnek NM, Shamsesfandabadi P, Peters KB, Saha A. 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. 2017 May;133(1):27–35.
Czarnek N, Clark K, Peters KB, Mazurowski MA. Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. J Neurooncol. 2017 Mar;132(1):55–62.
Tanpitukpongse TP, Mazurowski MA, Ikhena J, Petrella JR, 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. 2017 Mar;38(3):546–52.
Harowicz MR, Marks JR, Kelly Marcom P, Mazurowski MA. 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. 2017.
Harowicz MR, Robinson TJ, Dinan MA, Saha A, Marks JR, Marcom PK, et al. Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset. Breast Cancer Res Treat. 2017 Feb;162(1):1–10.
Mazurowski MA, Clark K, Czarnek NM, Shamsesfandabadi P, Peters KB, Saha A. 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. 2017.
Paredes D, Saha A, Mazurowski MA. 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. 2017.
Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. 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. 2017.
Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. 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. 2017.
Harowicz MR, Marks JR, Kelly Marcom P, Mazurowski MA. 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. 2017.
Ren B, Mazurowski MA. 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. 2017.
Wang M, Grimm LJ, Mazurowski MA. A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases. Expert Systems with Applications. 2016 Dec 1;64:490–9.
Wang M, Zhang J, Grimm LJ, Ghate SV, Walsh R, Johnson KS, et al. Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. Expert Systems with Applications. 2016 Sep 1;56:1–8.
Saha A, Grimm LJ, Harowicz M, Ghate SV, Kim C, Walsh R, et al. Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics. Med Phys. 2016 Aug;43(8):4558.
Mazurowski MA. Author's Reply. J Am Coll Radiol. 2016 Feb;13(2):121–2.
Grimm LJ, Zhang J, Lo JY, Johnson KS, Ghate SV, Walsh R, et al. Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns. J Am Coll Radiol. 2016 Feb;13(2):198–202.
Czarnek NM, Clark K, Peters KB, Collins LM, Mazurowski MA. 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. 2016.
Mazurowski MA, Czarnek NM, Collins LM, Peters KB, Clark K. Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape - Preliminary data. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Wang M, Zhang J, Grimm LJ, Ghate SV, Walsh R, Johnson KS, et al. 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. 2016.
Mazurowski MA, Grimm LJ, Zhang J, Marcom PK, Yoon SC, Kim C, et al. Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms. Eur J Radiol. 2015 Nov;84(11):2117–22.
Grimm LJ, Zhang J, Mazurowski MA. 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. 2015 Oct;42(4):902–7.
Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol. 2015 Aug;12(8):862–6.
Zhang J, Grimm LJ, Lo JY, Johnson KS, Ghate SV, Walsh R, et al. Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis? J Am Coll Radiol. 2015 Jul;12(7):728–32.
Zhang J, Silber JI, Mazurowski MA. Modeling false positive error making patterns in radiology trainees for improved mammography education. J Biomed Inform. 2015 Apr;54:50–7.
Tourassi GD, Mazurowski MA. Case-based CAD systems in breast imaging. In: Computer-Aided Detection and Diagnosis in Medical Imaging. 2015. p. 95–109.
Grimm LJ, Zhang J, Johnson KS, Lo JY, Mazurowski MA. 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. 2015.
Mazurowski MA, Zhang J, Peters KB, Hobbs H. Computer-extracted MR imaging features are associated with survival in glioblastoma patients. J Neurooncol. 2014 Dec;120(3):483–8.
Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology. 2014 Nov;273(2):365–72.
Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon SC, Mazurowski MA. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents. Med Phys. 2014 Sep;41(9):091907.
Grimm LJ, Kuzmiak CM, Ghate SV, Yoon SC, Mazurowski MA. Radiology resident mammography training: interpretation difficulty and error-making patterns. Acad Radiol. 2014 Jul;21(7):888–92.
Grimm LJ, Shapiro LM, Singhapricha T, Mazurowski MA, Desser TS, Maxfield CM. Predictors of an academic career on radiology residency applications. Acad Radiol. 2014 May;21(5):685–90.
Zhang J, Barboriak DP, Hobbs H, Mazurowski MA. A fully automatic extraction of magnetic resonance image features in glioblastoma patients. Med Phys. 2014 Apr;41(4):042301.
Grimm LJ, Ghate SV, Yoon SC, Kuzmiak CM, Kim C, Mazurowski MA. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features. Med Phys. 2014 Mar;41(3):031909.
Mazurowski MA, Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon S. 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. 2014.
Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro Oncol. 2013 Oct;15(10):1389–94.
Mazurowski MA. Estimating confidence of individual rating predictions in collaborative filtering recommender systems. Expert Systems with Applications. 2013 Aug 1;40(10):3847–57.
Mazurowski MA. Difficulty of mammographic cases in the context of resident training: Preliminary experimental data. Proceedings of SPIE - The International Society for Optical Engineering. 2013 Jun 14;8673.
Mazurowski MA, Barnhart HX, Baker JA, Tourassi GD. Identifying error-making patterns in assessment of mammographic BI-RADS descriptors among radiology residents using statistical pattern recognition. Acad Radiol. 2012 Jul;19(7):865–71.
Malof JM, Mazurowski MA, Tourassi GD. The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support. Neural Netw. 2012 Jan;25(1):141–5.
Mazurowski MA, Lo JY, Harrawood BP, Tourassi GD. Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis. J Biomed Inform. 2011 Oct;44(5):815–23.
Mazurowski MA, Tourassi GD. 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. 2011 Jul 7;
Mazurowski MA, Tourassi GD. 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. 2011 May 16;7966.
Mazurowski MA, Malof JM, Tourassi GD. Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support. Phys Med Biol. 2011 Jan 21;56(2):473–89.
Mazurowski MA, Lo JY, Tourassi GD. User modeling for improved computer-aided training in radiology: Initial experience. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2010.
Tourassi GD, Mazurowski MA, Harrawood BP, Krupinski EA. Exploring the potential of context-sensitive CADe in screening mammography. Med Phys. 2010 Nov;37(11):5728–36.
Mazurowski MA, Baker JA, Barnhart HX, Tourassi GD. Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments. Med Phys. 2010 Mar;37(3):1152–60.
Tourassi GD, Mazurowski MA, Krupinski EA. 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. 2010.
Mazurowski MA, Tourassi GD. Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy. Proceedings of the International Joint Conference on Neural Networks. 2009 Nov 18;2045–9.
Malof JM, Mazurowski MA, Tourassi GD. 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. 2009 Nov 18;1975–80.
Zurada JM, Mazurowski MA, Ragade R, Abdullin A, Wojtudiak J, Gentle J. Building virtual community in computational intelligence and machine learning. IEEE Computational Intelligence Magazine. 2009 Nov 6;4(1).
Mazurowski MA, Zurada JM, Tourassi GD. 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. 2009 Jul;36(7):2976–84.
Mazurowski MA, Malof JM, Zurada JM, Tourassi GD. 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. 2009 Jun 15;7260.
Mazurowski MA, Tourassi GD. Relational representation for improved decisions with an information-theoretic CADe system: Initial experience. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2009 Jun 15;7260.
Zurada JM, Aizenberg I, Mazurowski MA. Learning in networks: Complex-valued neurons, pruning, and rule extraction. 2008 4th International IEEE Conference Intelligent Systems, IS 2008. 2008 Dec 1;1:115–20.
Zurada JM, Wojtusiak J, Chowdhury F, Gentle JE, Jeannot CJ, Mazurowski MA. Computational intelligence virtual community: Framework and implementation issues. Proceedings of the International Joint Conference on Neural Networks. 2008 Nov 24;3153–7.
Mazurowski MA, Zurada JM, Tourassi GD. Selection of examples in case-based computer-aided decision systems. Phys Med Biol. 2008 Nov 7;53(21):6079–96.
Mazurowski MA, Zurada JM, Tourassi GD. 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). 2008 Sep 9;5116 LNCS:366–70.
Mazurowski MA, Harrawood BP, Zurada JM, Tourassi GD. 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. 2008 Jun 18;6917.
Mazurowski MA, Zurada JM, Tourassi GD. 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. 2008 Jun 2;6915.
Mazurowski MA, Habas PA, Zurada JM, Tourassi GD. Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography. Phys Med Biol. 2008 Feb 21;53(4):895–908.
Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 2008;21(2–3):427–36.
Mazurowski MA, Habas PA, Zurada JM, Tourassi GD. Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007 Dec 1;600–5.
Mazurowski MA, Habas PA, Zurada JM, Tourassi GD. 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. 2007 Dec 1;2005–9.
Tourassi GD, Jesneck JL, Mazurowski MA, Habas PA. Stacked generalization in computer-assisted decision systems: Empirical comparison of data handling schemes. IEEE International Conference on Neural Networks - Conference Proceedings. 2007 Dec 1;1343–7.
Mazurowski MA, Zurada JM. Solving decentralized multi-agent control problems with genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007 Dec 1;1029–34.
Mazurowski MA, Zurada JM. Solving multi-agent control problems using particle swarm optimization. Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007. 2007 Sep 25;105–11.
Mazurowski MA, Zurada JM. Emergence of communication in multi-agent systems using reinforcement learning. 2006 IEEE International Conference on Computational Cybernetics, ICCC. 2006 Dec 1;
Mazurowski MA, Szecówka PM. Limitations of sensitivity analysis for neural networks in cases with dependent inputs. 2006 IEEE International Conference on Computational Cybernetics, ICCC. 2006 Dec 1;
Szecówka PM, Szczurek A, Mazurowski MA, Licznerski BW, Pichler F. 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). 2005 Jan 1;3643 LNCS:27–32.

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