| Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? G Urban, KJ Geras, S Ebrahimi Kahou, O Aslan, S Wang, R Caruana, ... arXiv preprint arXiv:1603.05691, 2016 | 170 | 2016 |
| fastMRI: An open dataset and benchmarks for accelerated MRI J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang, MJ Muckley, A Defazio, ... arXiv preprint arXiv:1811.08839, 2018 | 168 | 2018 |
| High-resolution breast cancer screening with multi-view deep convolutional neural networks KJ Geras, S Wolfson, Y Shen, N Wu, S Kim, E Kim, L Heacock, U Parikh, ... arXiv preprint arXiv:1703.07047, 2017 | 125 | 2017 |
| Deep neural networks improve radiologists’ performance in breast cancer screening N Wu, J Phang, J Park, Y Shen, Z Huang, M Zorin, S Jastrzębski, T Févry, ... IEEE transactions on medical imaging 39 (4), 1184-1194, 2019 | 124 | 2019 |
| Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives KJ Geras, RM Mann, L Moy Radiology 293 (2), 246-259, 2019 | 59 | 2019 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... JAMA network open 3 (3), e200265-e200265, 2020 | 52 | 2020 |
| Blending LSTMs into CNNs KJ Geras, A Mohamed, R Caruana, G Urban, S Wang, O Aslan, ... arXiv preprint arXiv:1511.06433, 2015 | 47* | 2015 |
| Breast density classification with deep convolutional neural networks N Wu, KJ Geras, Y Shen, J Su, SG Kim, E Kim, S Wolfson, L Moy, K Cho 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 36 | 2018 |
| fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning F Knoll, J Zbontar, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, ... Radiology: Artificial Intelligence 2 (1), e190007, 2020 | 35 | 2020 |
| Scheduled denoising autoencoders KJ Geras, C Sutton arXiv preprint arXiv:1406.3269, 2014 | 33 | 2014 |
| New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence Y Gao, KJ Geras, AA Lewin, L Moy American Journal of Roentgenology 212 (2), 300-307, 2019 | 27 | 2019 |
| Analysis of deep neural networks with extended data jacobian matrix S Wang, A Mohamed, R Caruana, J Bilmes, M Plilipose, M Richardson, ... International Conference on Machine Learning, 718-726, 2016 | 27 | 2016 |
| The break-even point on optimization trajectories of deep neural networks S Jastrzebski, M Szymczak, S Fort, D Arpit, J Tabor, K Cho, K Geras arXiv preprint arXiv:2002.09572, 2020 | 24 | 2020 |
| Isoelastic agents and wealth updates in machine learning markets J Millin, K Geras, AJ Storkey Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012 | 22* | 2012 |
| Machine learning in breast MRI B Reig, L Heacock, KJ Geras, L Moy Journal of Magnetic Resonance Imaging 52 (4), 998-1018, 2020 | 20 | 2020 |
| Joint model training O Aslan, R Caruana, MR Richardson, A Mohamed, M Philipose, K Geras, ... US Patent App. 15/195,894, 2017 | 16 | 2017 |
| Multiple-source cross-validation K Geras, C Sutton International Conference on Machine Learning, 1292-1300, 2013 | 16 | 2013 |
| Classifier-agnostic saliency map extraction K Żołna, KJ Geras, K Cho Computer Vision and Image Understanding, 102969, 2020 | 11 | 2020 |
| An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization Y Shen, N Wu, J Phang, J Park, K Liu, S Tyagi, L Heacock, SG Kim, L Moy, ... Medical image analysis 68, 101908, 2021 | 9 | 2021 |
| Globally-aware multiple instance classifier for breast cancer screening Y Shen, N Wu, J Phang, J Park, G Kim, L Moy, K Cho, KJ Geras International Workshop on Machine Learning in Medical Imaging, 18-26, 2019 | 8 | 2019 |
Linda MoyNew York University School of MedicineVerified email at nyulangone.org
