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Jungkyu Park
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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
5642019
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
1502021
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
1292021
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
1252021
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
332019
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
312019
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
162020
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
11*2021
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
102021
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
82019
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
72023
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
52019
Deep neural networks improve radiologists’ performance in breast cancer screening. arXiv. 2019
N Wu, J Phang, J Park, Y Shen, Z Huang, M Zorin
Accessed, 2019
52019
Investigating and simplifying masking-based saliency methods for model interpretability
J Phang, J Park, KJ Geras
arXiv preprint arXiv:2010.09750, 2020
42020
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department (preprint)
FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ...
32020
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, 2023
1*2023
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
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
2023
An efficient deep neural network to find small objects in large 3D images
J Park, J Chłędowski, S Jastrzębski, J Witowski, Y Xu, L Du, S Gaddam, ...
arXiv preprint arXiv:2210.08645, 2022
2022
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