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Mateusz Buda
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A systematic study of the class imbalance problem in convolutional neural networks
M Buda, A Maki, MA Mazurowski
Neural Networks 106, 249-259, 2018
28962018
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
6182018
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
3272019
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
1972019
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
1282019
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images
M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, ...
JAMA network open 4 (8), e2119100-e2119100, 2021
106*2021
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
78*2020
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
402020
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
252020
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
A Swiecicki, N Konz, M Buda, MA Mazurowski
Scientific Reports 11 (1), 1-13, 2021
202021
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis
N Konz, M Buda, H Gu, A Saha, J Yang, J Chłędowski, J Park, J Witowski, ...
JAMA Network Open 6 (2), e230524-e230524, 2023
142023
Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset
J Weng, B Wildman-Tobriner, M Buda, J Yang, LM Ho, BC Allen, ...
Clinical Imaging 99, 60-66, 2023
62023
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
52020
Breast Cancer Screening–Digital Breast Tomosynthesis (BCS-DBT)
M Buda, A Saha, R Walsh, S Ghate, N Li, A Swiecicki, JY Lo, J Yang, ...
Type: dataset, 2020
52020
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
32020
Application of AI tools to the inventory of technical and transportation infrastructure based on UAV data
P Zachar, W Ostrowski, K Bakuła, M Buda, M Foltyn, R Palak, ...
2022
OCR system for text recognition in images of specified type
M Dłuś, M Buda, P Kobojek
Zakład Zastosowań Informatyki i Metod Numerycznych, 2015
2015
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학술자료 1–17