Jared Dunnmon
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Learning to compose domain-specific transformations for data augmentation
AJ Ratner, H Ehrenberg, Z Hussain, J Dunnmon, C Ré
Advances in neural information processing systems, 3236-3246, 2017
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L Oakden-Rayner, J Dunnmon, G Carneiro, C Ré
Proceedings of the ACM Conference on Health, Inference, and Learning, 151-159, 2020
Power extraction from aeroelastic limit cycle oscillations
JA Dunnmon, SC Stanton, BP Mann, EH Dowell
Journal of Fluids and Structures 27 (8), 1182-1198, 2011
Assessment of convolutional neural networks for automated classification of chest radiographs
JA Dunnmon, D Yi, CP Langlotz, C Ré, DL Rubin, MP Lungren
Radiology 290 (2), 537-544, 2019
Training complex models with multi-task weak supervision
A Ratner, B Hancock, J Dunnmon, F Sala, S Pandey, C Ré
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4763-4771, 2019
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
NS Sohoni, JA Dunnmon, G Angus, A Gu, C Ré
arXiv preprint arXiv:2011.12945, 2020
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
JA Fries, P Varma, VS Chen, K Xiao, H Tejeda, P Saha, J Dunnmon, ...
Nature communications 10 (1), 1-10, 2019
PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging
SC Huang, T Kothari, I Banerjee, C Chute, RL Ball, N Borus, A Huang, ...
npj Digital Medicine 3 (1), 1-9, 2020
Domino: Discovering systematic errors with cross-modal embeddings
S Eyuboglu, M Varma, K Saab, JB Delbrouck, C Lee-Messer, J Dunnmon, ...
arXiv preprint arXiv:2203.14960, 2022
Self-supervised graph neural networks for improved electroencephalographic seizure analysis
S Tang, JA Dunnmon, K Saab, X Zhang, Q Huang, F Dubost, DL Rubin, ...
arXiv preprint arXiv:2104.08336, 2021
Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset
S Tang, A Ghorbani, R Yamashita, S Rehman, JA Dunnmon, J Zou, ...
Scientific reports 11 (1), 1-9, 2021
Weak supervision as an efficient approach for automated seizure detection in electroencephalography
K Saab, J Dunnmon, C Ré, D Rubin, C Lee-Messer
npj Digital Medicine 3 (1), 1-12, 2020
Automated abnormality detection in lower extremity radiographs using deep learning
M Varma, M Lu, R Gardner, J Dunnmon, N Khandwala, P Rajpurkar, ...
Nature Machine Intelligence 1 (12), 578-583, 2019
Snorkel metal: Weak supervision for multi-task learning
A Ratner, B Hancock, J Dunnmon, R Goldman, C Ré
Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018
Cross-modal data programming enables rapid medical machine learning
JA Dunnmon, AJ Ratner, K Saab, N Khandwala, M Markert, H Sagreiya, ...
Patterns 1 (2), 100019, 2020
Optimizing and visualizing deep learning for benign/malignant classification in breast tumors
D Yi, RL Sawyer, D Cohn III, J Dunnmon, C Lam, X Xiao, D Rubin
arXiv preprint arXiv:1705.06362, 2017
An investigation of internal flame structure in porous media combustion via X-ray Computed Tomography
J Dunnmon, S Sobhani, M Wu, R Fahrig, M Ihme
Proceedings of the Combustion Institute 36 (3), 4399-4408, 2017
Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT
S Eyuboglu, G Angus, BN Patel, A Pareek, G Davidzon, J Long, ...
Nature communications 12 (1), 1-15, 2021
Ivy: Instrumental variable synthesis for causal inference
Z Kuang, F Sala, N Sohoni, S Wu, A Córdova-Palomera, J Dunnmon, ...
International Conference on Artificial Intelligence and Statistics, 398-410, 2020
Observational Supervision for Medical Image Classification Using Gaze Data
K Saab, SM Hooper, NS Sohoni, J Parmar, B Pogatchnik, S Wu, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2021
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