| A systematic study of the class imbalance problem in convolutional neural networks M Buda, A Maki, MA Mazurowski Neural Networks 106, 249-259, 2018 | 2927 | 2018 |
| Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance MA Mazurowski, PA Habas, JM Zurada, JY Lo, JA Baker, GD Tourassi Neural Networks 21 (2-3), 427-436, 2008 | 1087 | 2008 |
| 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, 2019 | 618 | 2019 |
| Segment anything model for medical image analysis: an experimental study MA Mazurowski, H Dong, H Gu, J Yang, N Konz, Y Zhang Medical Image Analysis 89, 102918, 2023 | 479 | 2023 |
| 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 and medicine 109, 218-225, 2019 | 328 | 2019 |
| Radiogenomics: What It Is and Why It Is Important MA Mazurowski Journal of the American College of Radiology 12 (8), 862-866, 2015 | 318 | 2015 |
| Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing EA AlBadawy, A Saha, MA Mazurowski Medical physics 45 (3), 1150-1158, 2018 | 267 | 2018 |
| Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging MA Mazurowski, J Zhang, LJ Grimm, SC Yoon, JI Silber Radiology 273 (2), 365-372, 2014 | 264 | 2014 |
| A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features A Saha, MR Harowicz, LJ Grimm, CE Kim, SV Ghate, R Walsh, ... British journal of cancer 119 (4), 508-516, 2018 | 262 | 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 | 198 | 2019 |
| 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 EH Cain, A Saha, MR Harowicz, JR Marks, PK Marcom, MA Mazurowski Breast cancer research and treatment 173, 455-463, 2019 | 196 | 2019 |
| Hierarchical convolutional neural networks for segmentation of breast tumors in mri with application to radiogenomics J Zhang, A Saha, Z Zhu, MA Mazurowski IEEE transactions on medical imaging 38 (2), 435-447, 2019 | 184 | 2019 |
| Deep Learning for identifying radiogenomic associations in breast cancer Z Zhu, E Albadawy, A Saha, J Zhang, MR Harowicz, MA Mazurowski Computers in biology and medicine 109, 85-90, 2019 | 164 | 2019 |
| 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 … LJ Grimm, J Zhang, MA Mazurowski Journal of Magnetic Resonance Imaging 42 (4), 902-907, 2015 | 161 | 2015 |
| 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 … MA Mazurowski, K Clark, NM Czarnek, P Shamsesfandabadi, KB Peters, ... Journal of neuro-oncology 133, 27-35, 2017 | 143 | 2017 |
| 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 | 128 | 2019 |
| Imaging descriptors improve the predictive power of survival models for glioblastoma patients MA Mazurowski, A Desjardins, JM Malof Neuro-oncology 15 (10), 1389-1394, 2013 | 125 | 2013 |
| 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 | 108* | 2021 |
| Artificial intelligence may cause a significant disruption to the radiology workforce MA Mazurowski Journal of the American College of Radiology 16 (8), 1077-1082, 2019 | 96 | 2019 |
| Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists A Swiecicki, N Li, J O'Donnell, N Said, J Yang, RC Mather, WA Jiranek, ... Computers in biology and medicine 133, 104334, 2021 | 85 | 2021 |
Ashirbani SahaAssistant Professor, Department of Oncology, McMaster Universityایمیل تأیید شده در mcmaster.ca
