15 citations · 28 across the 4 of their papers we have counts for
5 papers
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…
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…
Prediction of Small Molecule Kinase Inhibitors for Chemotherapy Using Deep Learning
Niranjan Balachandar, Christine Liu, Winston Wang
The current state of cancer therapeutics has been moving away from one-size-fits-all cytotoxic chemotherapy, and towards a more individualized and specific approach involving the t…
Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning
Niranjan Balachandar, Justin Dieter, Govardana Sachithanandam Ramachandran
There are many AI tasks involving multiple interacting agents where agents should learn to cooperate and collaborate to effectively perform the task. Here we develop and evaluate v…
Institutionally Distributed Deep Learning Networks
Ken Chang, Niranjan Balachandar, Carson K Lam +6
Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achi…