| 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 | 293 | 2019 |
| High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks KJ Geras, S Wolfson, Y Shen, S Kim, L Moy, K Cho arXiv preprint arXiv:1703.07047, 2017 | 184 | 2017 |
| 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 | 159 | 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 | 64 | 2018 |
| 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 | 63 | 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), 1-11, 2021 | 56 | 2021 |
| 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, 2020 | 43 | 2020 |
| 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 | 20 | 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 (1), 2021 | 19 | 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 | 15 | 2019 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open. 2020; 3 (3): 200265 T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... | 10 | 2020 |
| Weakly-supervised high-resolution segmentation of mammography images for breast cancer diagnosis K Liu, Y Shen, N Wu, J Chłędowski, C Fernandez-Granda, KJ Geras Proceedings of machine learning research 143, 268, 2021 | 6 | 2021 |
| Adaptive early-learning correction for segmentation from noisy annotations S Liu, K Liu, W Zhu, Y Shen, C Fernandez-Granda Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 4 | 2022 |
| Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms N Wu, Z Huang, Y Shen, J Park, J Phang, T Makino, S Kim, K Cho, ... arXiv preprint arXiv:2009.09282, 2020 | 3 | 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 (6), 1414-1423, 2021 | 2 | 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 | 1 | 2019 |
| An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzebski, ... arXiv preprint arXiv:2008.01774, 2020 | | 2020 |
Krzysztof J. GerasNew York UniversityZweryfikowany adres z nyu.edu
