Meeting Banner
Abstract #2418

Federated Multi-task Image Classification on Heterogeneous Medical data with Privacy Perversing

Shenjun Zhong1, Adam Morris2, Zhaolin Chen1, and Gary Egan1
1Monash Biomedical Imaging, Monash University, Australia, Melbourne, Australia, 2Monash eResearch Center, Monash University, Australia, Melbourne, Australia

There is a lack of pre-trained deep learning model weights on large scale medical image dataset, due to privacy concerns. Federated learning enables training deep networks while preserving privacy. This work explored co-training multi-task models on multiple heterogeneous datasets, and validated the usage of federated learning could serve the purpose of pre-trained weights for downstream tasks.

This abstract and the presentation materials are available to members only; a login is required.

Join Here