Mateusz Buda
タイトル
引用先
引用先
A systematic study of the class imbalance problem in convolutional neural networks
M Buda, A Maki, MA Mazurowski
Neural Networks 106, 249-259, 2018
7352018
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
MA Mazurowski, M Buda, A Saha, MR Bashir
Journal of Magnetic Resonance Imaging 49 (4), 939--954, 2018
146*2018
Management of thyroid nodules seen on US images: deep learning may match performance of radiologists
M Buda, B Wildman-Tobriner, JK Hoang, D Thayer, FN Tessler, ...
Radiology 292 (3), 695-701, 2019
402019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
M Buda, A Saha, MA Mazurowski
Computers in Biology & Medicine 109, 218-225, 2019
282019
Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility
B Wildman-Tobriner, M Buda, JK Hoang, WD Middleton, D Thayer, ...
Radiology 292 (1), 112-119, 2019
202019
MRI image harmonization using cycle-consistent generative adversarial network
G Modanwal, A Vellal, M Buda, MA Mazurowski
Medical Imaging 2020: Computer-Aided Diagnosis 11314, 259--264, 2020
82020
Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images
M Buda, B Wildman-Tobriner, K Castor, JK Hoang, MA Mazurowski
Ultrasound in Medicine & Biology 46 (2), 415-421, 2020
72020
Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms
A Swiecicki, N Said, J O'Donnell, M Buda, N Li, WA Jiranek, ...
Medical Imaging 2020: Computer-Aided Diagnosis 11314, 863--868, 2020
12020
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, ...
arXiv preprint arXiv:2011.07995, 2020
2020
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images
A Swiecicki, M Buda, A Saha, N Li, SV Ghate, R Walsh, MA Mazurowski
Medical Imaging 2020: Computer-Aided Diagnosis 11314, 514--519, 2020
2020
Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images
M Buda, EA AlBadawy, A Saha, MA Mazurowski
Radiology: Artificial Intelligence 2 (1), 2020
2020
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