362 citations · 917 across the 15 of their papers we have counts for
6 papers · 1 filter
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu +4
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in mul…
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…
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang +5
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each inst…
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…