An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging

no code implementations • 18 Jul 2021 • Liangqiong Qu, Niranjan Balachandar, Daniel L Rubin

In this paper, we investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew.

Federated Learning Privacy Preserving

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

no code implementations • 16 Nov 2020 • Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Arun, Liangqiong Qu, Katharina Hoebel, Jay Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L Rubin, Jayashree Kalpathy-Cramer

Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types.