| 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 | 884 | 2018 |
| 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 | 653 | 2019 |
| 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 | 371 | 2020 |
| 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 | 366 | 2020 |
| 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 | 314 | 2016 |
| 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 | 302 | 2017 |
| 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 | 300 | 2019 |
| 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 | 172 | 2021 |
| Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams Y Shen, FE Shamout, JR Oliver, J Witowski, K Kannan, J Park, N Wu, ... Nature communications 12 (1), 5645, 2021 | 170 | 2021 |
| 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 | 164 | 2020 |
| Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative K Leung, B Zhang, J Tan, Y Shen, KJ Geras, JS Babb, K Cho, G Chang, ... Radiology 296 (3), 584-593, 2020 | 162 | 2020 |
| An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ... npj Digital Medicine 4 (1), 1-11, 2021 | 138 | 2021 |
| 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 | 134 | 2019 |
| Machine learning in breast MRI B Reig, L Heacock, KJ Geras, L Moy Journal of magnetic resonance imaging 52 (4), 998-1018, 2020 | 133 | 2020 |
| 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 | 101 | 2018 |
| Joint model training O Aslan, R Caruana, MR Richardson, A Mohamed, M Philipose, K Geras, ... US Patent App. 15/195,894, 2017 | 94 | 2017 |
| Covid-19 prognosis via self-supervised representation learning and multi-image prediction A Sriram, M Muckley, K Sinha, F Shamout, J Pineau, KJ Geras, L Azour, ... arXiv preprint arXiv:2101.04909, 2021 | 89* | 2021 |
| Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks N Wu, S Jastrzebski, K Cho, KJ Geras International Conference on Machine Learning, 24043-24055, 2022 | 84 | 2022 |
| Blending LSTMs into CNNs KJ Geras, A Mohamed, R Caruana, G Urban, S Wang, O Aslan, ... arXiv preprint arXiv:1511.06433, 2015 | 75* | 2015 |
| Catastrophic fisher explosion: Early phase fisher matrix impacts generalization S Jastrzebski, D Arpit, O Astrand, GB Kerg, H Wang, C Xiong, R Socher, ... International Conference on Machine Learning, 4772-4784, 2021 | 65 | 2021 |
Linda MoyNew York University School of MedicineZweryfikowany adres z nyulangone.org
