| 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 | 419 | 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 | 406 | 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 | 267 | 2011 |
| 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 | 239 | 2020 |
| 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 | 239 | 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 | 236 | 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 | 138 | 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 | 137 | 2022 |
| 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 | 135 | 2019 |
| 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 | 115 | 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 | 97 | 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 | 84 | 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 | 84 | 2019 |
| 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 | 67 | 2020 |
| 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 | 65 | 2018 |
| 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 | 60 | 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 | 55 | 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 | 36 | 2021 |
| ViLMedic: a framework for research at the intersection of vision and language in medical AI JB Delbrouck, K Saab, M Varma, S Eyuboglu, P Chambon, J Dunnmon, ... Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 35 | 2022 |
| 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 | 34 | 2021 |
