Krzysztof J. Geras
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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
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
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
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
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
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
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
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
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
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
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
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
Machine learning in breast MRI
B Reig, L Heacock, KJ Geras, L Moy
Journal of Magnetic Resonance Imaging 52 (4), 998-1018, 2020
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
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
Joint model training
O Aslan, R Caruana, MR Richardson, A Mohamed, M Philipose, K Geras, ...
US Patent App. 15/195,894, 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
Blending LSTMs into CNNs
KJ Geras, A Mohamed, R Caruana, G Urban, S Wang, O Aslan, ...
arXiv preprint arXiv:1511.06433, 2015
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
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
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