Style Normalization In Histology With Federated Learning
Jing Ke, Yiqing Shen, Yizhou Lu
Abstract
The global cancer burden is on the rise, and Artificial Intelligence (AI) has become increasingly crucial to achieve more objective and efficient diagnosis in digital pathology. Current AI-assisted histopathology analysis methods need to address the following two issues. First, the color variations due to use of different stains need to be tackled such as with stain style transfer technique. Second, in parallel with heterogeneity, datasets from individual clinical institutions are characterized by privacy regulations, and thus need to be addressed such as with robust data-private collaborative training. In this paper, to address the color heterogeneity problem, we propose a novel generative adversarial network with one orchestrating generator and multiple distributed discriminators for stain style transfer. We also incorporate Federated Learning (FL) to further preserve data privacy and security from multiple data centers. We use a large cohort of histopathology datasets as a case study.