Nandita Bhaskhar

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 10

Top Topics

Chest Radiographs

Digital X Ray Images

Modeling Framework

Top Venues

IEEE Trans. Artif. Intell.

J. Biomed. Informatics

Publications

Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer
An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring. IEEE Trans. Artif. Intell. 5 (4) (2024)
Juanma Zambrano Chaves, Nandita Bhaskhar, Maayane Attias, Jean-Benoit Delbrouck, Daniel L. Rubin, Andreas M. Loening, Curtis P. Langlotz, Akshay Chaudhari
RaLEs: a Benchmark for Radiology Language Evaluations. NeurIPS (2023)
Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay S. Chaudhari
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. MIDL (2023)
Nandita Bhaskhar, Wui Ip, Jonathan H. Chen, Daniel L. Rubin
Clinical outcome prediction using observational supervision with electronic health records and audit logs. J. Biomed. Informatics 147 (2023)
Florian Dubost, Erin Hong, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing. WACV (2023)
Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay Chaudhari
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. CoRR (2023)
Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring. CoRR (2022)
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
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning. CoRR (2022)
Florian Dubost, Khaled Kamal Saab, Erin Hong, Daniel Yang Fu, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels. CoRR (2021)
Florian Dubost, Erin Hong, Daniel Y. Fu, Nandita Bhaskhar, Siyi Tang, Khaled Saab, Daniel L. Rubin, Jared Dunnmon, Christopher Lee-Messer
Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy. CoRR (2020)