| 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 | 619 | 2019 |
| 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 | 336 | 2019 |
| 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 | 264 | 2018 |
| 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 | 264 | 2018 |
| 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 | 194 | 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, 2018 | 183 | 2018 |
| 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 | 163 | 2019 |
| 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 | 142 | 2017 |
| 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 |
| Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a … MD McCradden, A Baba, A Saha, S Ahmad, K Boparai, P Fadaiefard, ... Canadian Medical Association Open Access Journal 8 (1), E90-E95, 2020 | 85 | 2020 |
| Effects of MRI scanner parameters on breast cancer radiomics A Saha, X Yu, D Sahoo, MA Mazurowski Expert systems with applications 87, 384-391, 2017 | 83 | 2017 |
| Generative AI for Brain Image Computing and Brain Network Computing: A Review C Gong, C Jing, X Chen, G Huang, A Saha, M Nieuwoudt, Y Hu, S Wang Frontiers in Neuroscience 17, 1203104, 2023 | 70 | 2023 |
| Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors A Saha, MR Harowicz, MA Mazurowski Medical physics 45 (7), 3076-3085, 2018 | 66 | 2018 |
| Utilizing image scales towards totally training free blind image quality assessment A Saha, QMJ Wu IEEE Transactions on Image Processing 24 (6), 1879-1892, 2015 | 64 | 2015 |
| Mutual spectral residual approach for multifocus image fusion A Saha, G Bhatnagar, QMJ Wu Digital Signal Processing 23 (4), 1121-1135, 2013 | 60 | 2013 |
| Full-reference image quality assessment by combining global and local distortion measures A Saha, QMJ Wu http://arxiv.org/abs/1412.5488, 2014 | 58 | 2014 |
| Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ Z Zhu, M Harowicz, J Zhang, A Saha, LJ Grimm, ES Hwang, ... Computers in biology and medicine 115, 103498, 2019 | 51 | 2019 |
| Relationship between background parenchymal enhancement on high-risk screening MRI and future breast cancer risk LJ Grimm, A Saha, SV Ghate, C Kim, MS Soo, SC Yoon, MA Mazurowski Academic radiology 26 (1), 69-75, 2019 | 51 | 2019 |
| A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models A Saha, MR Harowicz, W Wang, MA Mazurowski Journal of cancer research and clinical oncology 144, 799-807, 2018 | 51 | 2018 |
| Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset MR Harowicz, TJ Robinson, MA Dinan, A Saha, JR Marks, PK Marcom, ... Breast cancer research and treatment 162, 1-10, 2017 | 42 | 2017 |
Maciej A. MazurowskiAssociate Professor of Biostatistics & Bioinformatics, Radiology, Comp. Sci., ECE, Duke UniversityEmail yang diverifikasi di duke.edu
