Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy. - Florian Dubost; Erin Hong; Daniel Y. Fu; Nandita Bhaskhar; Siyi Tang; Khaled Saab 002; Daniel L. Rubin; Jared Dunnmon; Christopher Lee-Messer (CoRR, 2020)

Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels. - Florian Dubost; Khaled Kamal Saab; Erin Hong; Daniel Yang Fu; Max Pike; Siddharth Sharma; Siyi Tang; Nandita Bhaskhar; Christopher Lee-Messer; Daniel L. Rubin (CoRR, 2021)

TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring. - Nandita Bhaskhar; Daniel L. Rubin; Christopher Lee-Messer (CoRR, 2022)

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning. - Jeffrey Dominic; Nandita Bhaskhar; Arjun D. Desai; Andrew M. Schmidt; Elka B. Rubin; Beliz Gunel; Garry E. Gold; Brian A. Hargreaves; Leon Lenchik; Robert Boutin; Akshay S. Chaudhari (CoRR, 2022)

Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. - Rogier van der Sluijs; Nandita Bhaskhar; Daniel L. Rubin; Curtis P. Langlotz; Akshay S. Chaudhari (MIDL, 2023)

RaLEs: a Benchmark for Radiology Language Evaluations. - Juanma Zambrano Chaves; Nandita Bhaskhar; Maayane Attias; Jean-Benoit Delbrouck; Daniel L. Rubin; Andreas M. Loening; Curtis P. Langlotz; Akshay Chaudhari (NeurIPS, 2023)

Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing. - Florian Dubost; Erin Hong; Siyi Tang; Nandita Bhaskhar; Christopher Lee-Messer; Daniel L. Rubin (WACV, 2023)

Clinical outcome prediction using observational supervision with electronic health records and audit logs. - Nandita Bhaskhar; Wui Ip; Jonathan H. Chen; Daniel L. Rubin (J. Biomed. Informatics, 2023)

An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring. - Nandita Bhaskhar; Daniel L. Rubin; Christopher Lee-Messer (IEEE Trans. Artif. Intell., 2024)