Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods. - Daniel Y. Fu; Mayee F. Chen; Frederic Sala; Sarah M. Hooper; Kayvon Fatahalian; Christopher Ré (ICML, 2020)

Assessing Robustness to Noise: Low-Cost Head CT Triage. - Sarah M. Hooper; Jared A. Dunnmon; Matthew P. Lungren; Sanjiv Sam Gambhir; Christopher Ré; Adam S. Wang; Bhavik N. Patel (CoRR, 2020)

Cut out the annotator, keep the cutout: better segmentation with weak supervision. - Sarah M. Hooper; Michael Wornow; Ying Hang Seah; Peter Kellman; Hui Xue 006; Frederic Sala; Curtis P. Langlotz; Christopher Ré (ICLR, 2021)

Observational Supervision for Medical Image Classification Using Gaze Data. - Khaled Saab 002; Sarah M. Hooper; Nimit Sharad Sohoni; Jupinder Parmar; Brian Pogatchnik; Sen Wu 002; Jared A. Dunnmon; Hongyang R. Zhang; Daniel L. Rubin; Christopher Ré (MICCAI (2), 2021)

Multi-tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas. - Mirwais Wardak; Sarah M. Hooper; Christiaan Schiepers; Wei Chen; Carina Mari Aparici; Guido A. Davidzon; Ophir Vermesh; Timothy F. Cloughesy; Sung-Cheng Huang; Sanjiv Sam Gambhir (PRIME@MICCAI, 2022)

Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity. - Khaled Saab 002; Sarah M. Hooper; Mayee F. Chen; Michael Zhang; Daniel L. Rubin; Christopher Ré (MLHC, 2022)

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data. - Maya Varma; Jean-Benoit Delbrouck; Sarah M. Hooper; Akshay Chaudhari; Curtis P. Langlotz (ICCV, 2023)

A case for reframing automated medical image classification as segmentation. - Sarah M. Hooper; Mayee F. Chen; Khaled Saab 002; Kush Bhatia; Curtis P. Langlotz; Christopher Ré (NeurIPS, 2023)

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis. - Sarah M. Hooper; Hui Xue (ML4H@NeurIPS, 2024)

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis. - Sarah M. Hooper; Hui Xue 006 (CoRR, 2025)

SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation. - Hui Xue 006; Sarah M. Hooper; Iain Pierce; Rhodri H. Davies; John Stairs; Joseph Naegele; Adrienne E. Campbell-Washburn; Charlotte Manisty; James C. Moon; Thomas A. Treibel; Peter Kellman; Michael S. Hansen (CoRR, 2025)