1.3k citations · 2.2k across the 29 of their papers we have counts for
3 papers · 2 filters
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, Daniel L Rubin
Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. However, its distributed nature o…
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
Miao Zhang, Liangqiong Qu, Praveer Singh +2
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different instit…
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…