Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, Jichen Yang, Lisa M. Ho, Brian C. Allen, Wendy L. Ehieli, Chad M. Miller, Jikai Zhang, and Maciej A. Mazurowski. Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset Clinical Imaging, 2023.

Mateusz Buda, Nicholas Konz, Mateusz Buda, Hanxue Gu, Ashirbani Saha, Jichen Yang, Jakub Chłędowski, Jungkyu Park, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis JAMA Network Open, 2023.

Paulina Zachar, Wojciech Ostrowski, Krzysztof Bakuła, Mateusz Buda, Maksymilian Foltyn, Radosław Palak, Konrad Sosnowicz. "Application of AI tools to the inventory of technical and transportation infrastructure based on UAV data." FIG Congress, 2022.

Mateusz Buda, Ashirbani Saha, Ruth Walsh, Sujata Ghate, Nianyi Li, Albert Święcicki, Joseph Y. Lo, Maciej A. Mazurowski. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Network Open, 2021.

Albert Święcicki, Nicholas Konz, Mateusz Buda, Maciej A. Mazurowski. A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis. Scientific Reports, 2021.

Albert Święcicki, Mateusz Buda, Ashirbani Saha, Nianyi Li, Sujata V Ghate, Ruth Walsh, Maciej A Mazurowski. Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images. Medical Imaging 2020: Computer-Aided Diagnosis.

Albert Święcicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A Jiranek, Maciej A Mazurowski. Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. Medical Imaging 2020: Computer-Aided Diagnosis.

Gourav Modanwal, Adithya Vellal, Mateusz Buda, Maciej A Mazurowski. MRI image harmonization using cycle-consistent generative adversarial network. Medical Imaging 2020: Computer-Aided Diagnosis.

Mateusz Buda, Ehab A AlBadawy, Ashirbani Saha, Maciej A Mazurowski. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiology: Artificial Intelligence, 2020.

Mateusz Buda, Benjamin Wildman-Tobriner, Kerry Castor, Jenny K Hoang, Maciej A Mazurowski. Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images. Ultrasound in Medicine & Biology, 2020.

Mateusz Buda, Benjamin Wildman-Tobriner, Jenny K Hoang, David Thayer, Franklin N Tessler, William D Middleton, Maciej A Mazurowski. Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. Radiology, 2019.

Benjamin Wildman-Tobriner, Mateusz Buda, Jenny K Hoang, William D Middleton, David Thayer, Ryan G Short, Franklin N Tessler, Maciej A Mazurowski. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Radiology, 2019.

Mateusz Buda, Ashirbani Saha, Maciej A Mazurowski. Association of Genomic Subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Computers in Biology and Medicine, 2019.

Maciej A Mazurowski, Mateusz Buda, Ashirbani Saha, 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. Journal of Magnetic Resonance Imaging, 2018.

Mateusz Buda, Atsuto Maki, Maciej A Mazurowski. A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 2018.

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