1.5k citations · 1.9k across the 16 of their papers we have counts for
6 papers · 1 filter
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Sourav Kumar, A. Lakshminarayanan, Ken Chang +5
Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not…
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Charles Lu, Syed Rakin Ahmed, Praveer Singh +1
Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical dep…
Deploying clinical machine learning? Consider the following...
Charles Lu, Ken Chang, Praveer Singh +5
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world c…
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…
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…