134 citations · 431 across the 17 of their papers we have counts for
4 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…
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…
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang +3
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-cen…