Jungkyu Park
Cytowane przez
Cytowane przez
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
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, 101908, 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
The evolution of shared concepts in changing populations
J Park, S Tauber, KA Jameson, L Narens
Review of Philosophy and Psychology 10 (3), 479-498, 2019
The NYU breast cancer screening dataset v1
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
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, ...
arXiv preprint arXiv:2008.01774, 2020
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
J Phang, J Park, KJ Geras
arXiv preprint arXiv:2010.09750, 2020
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
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
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
J Park, S Tauber, KA Jameson, L Narens
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Prace 1–11