Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
Journal:
arXiv
Published Date:
Jan 8, 2025
Abstract
Federated- and Continual Learning have been established as approaches to
enable privacy-aware learning on continuously changing data, as required for
deploying AI systems in histopathology images. However, data shifts can occur
in a dynamic world, spatially between institutions and temporally, due to
changing data over time. This leads to two issues: Client Drift, where the
central model degrades from aggregating data from clients trained on shifted
data, and Catastrophic Forgetting, from temporal shifts such as changes in
patient populations. Both tend to degrade the model's performance of previously
seen data or spatially distributed training. Despite both problems arising from
the same underlying problem of data shifts, existing research addresses them
only individually. In this work, we introduce a method that can jointly
alleviate Client Drift and Catastrophic Forgetting by using our proposed
Dynamic Barlow Continuity that evaluates client updates on a public reference
dataset and uses this to guide the training process to a spatially and
temporally shift-invariant model. We evaluate our approach on the
histopathology datasets BCSS and Semicol and prove our method to be highly
effective by jointly improving the dice score as much as from 15.8% to 71.6% in
Client Drift and from 42.5% to 62.8% in Catastrophic Forgetting. This enables
Dynamic Learning by establishing spatio-temporal shift-invariance.
