4 citations · 11 across the 5 of their papers we have counts for
6 papers
FL Games: A Federated Learning Framework for Distribution Shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
FL Games: A federated learning framework for distribution shifts
Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…
Addressing catastrophic forgetting for medical domain expansion
Sharut Gupta, Praveer Singh, Ken Chang +13
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Sharut Gupta, Praveer Singh, Ken Chang +9
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institutio…
Towards Trainable Saliency Maps in Medical Imaging
Mehak Aggarwal, Nishanth Arun, Sharut Gupta +9
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…