27 citations · 55 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, Miao Zhang +1
Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging…
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar +3
Self-supervised contrastive learning between pairs of multiple views of the same image has been shown to successfully leverage unlabeled data to produce meaningful visual represent…