| 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 | 713 | 2019 |
| 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, 5645, 2021 | 193 | 2021 |
| 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 | 192 | 2021 |
| 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), 80, 2021 | 147 | 2021 |
| The NYU breast cancer screening dataset v1. 0 N Wu, J Phang, J Park, Y Shen, SG Kim, L Heacock, L Moy, K Cho, ... New York Univ., New York, NY, USA, Tech. Rep, 2019 | 38 | 2019 |
| 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 Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 38 | 2019 |
| Improving the ability of deep neural networks to use information from multiple views in breast cancer screening N Wu, S Jastrzębski, J Park, L Moy, K Cho, KJ Geras Medical Imaging with Deep Learning, 827-842, 2020 | 21 | 2020 |
| Reducing false-positive biopsies using deep neural networks that utilize both local and global image context of screening mammograms N Wu, Z Huang, Y Shen, J Park, J Phang, T Makino, S Gene Kim, K Cho, ... Journal of Digital Imaging 34, 1414-1423, 2021 | 17* | 2021 |
| 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 | 16 | 2023 |
| Lessons from the first DBTex Challenge J Park, Y Shoshan, R Martí, P Gómez del Campo, V Ratner, D Khapun, ... Nature Machine Intelligence 3 (8), 735-736, 2021 | 11 | 2021 |
| Screening mammogram classification with prior exams J Park, J Phang, Y Shen, N Wu, S Kim, L Moy, K Cho, KJ Geras arXiv preprint arXiv:1907.13057, 2019 | 9 | 2019 |
| Investigating and simplifying masking-based saliency methods for model interpretability J Phang, J Park, KJ Geras arXiv preprint arXiv:2010.09750, 2020 | 8 | 2020 |
| The evolution of shared concepts in changing populations J Park, S Tauber, KA Jameson, L Narens Review of Philosophy and Psychology 10, 479-498, 2019 | 8 | 2019 |
| An efficient deep neural network to classify large 3D images with small objects J Park, J Chłędowski, S Jastrzębski, J Witowski, Y Xu, L Du, S Gaddam, ... IEEE transactions on medical imaging 43 (1), 351-365, 2023 | 7* | 2023 |
| Leveraging transformers to improve breast cancer classification and risk assessment with multi-modal and longitudinal data Y Shen, J Park, F Yeung, E Goldberg, L Heacock, F Shamout, KJ Geras arXiv preprint arXiv:2311.03217, 2023 | 5 | 2023 |
| A training regime to learn unified representations from complementary breast imaging modalities U Sharma, J Park, L Heacock, S Chopra, K Geras arXiv preprint arXiv:2408.08560, 2024 | | 2024 |
| Exploring synthesizing 2D mammograms from 3D digital breast tomosynthesis images J Chłędowski, J Park, KJ Geras 2023 International Conference on Digital Image Computing: Techniques and …, 2023 | | 2023 |
| Total Knee Replacement Prediction using Twin Class Distribution Estimation C Zhang, S Chen, H Huang, HR Rajamohan, J Park, N Kasmanoff, K Cho, ... | | |
Krzysztof J. GerasNew York University確認したメール アドレス: nyu.edu