Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging

TL;DR

This paper introduces Federated Alternate Training (FAT), a novel federated learning framework that leverages both annotated and unannotated medical imaging data silos to improve segmentation models while preserving privacy.

Contribution

FAT is the first framework to alternate training between annotated and unannotated data silos in federated learning for medical imaging segmentation.

Findings

FAT achieves promising segmentation performance on KiTS19 and FeTS2021 datasets.

The framework effectively utilizes unannotated data to enhance model accuracy.

FAT maintains data privacy while leveraging unannotated data for training.

Abstract

Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets. However, most current FL-based medical imaging works assume silos have ground truth labels for training. In practice, label acquisition in the medical field is challenging as it often requires extensive labor and time costs. To address this challenge and leverage the unannotated data silos to improve modeling, we propose an alternate training-based framework, Federated Alternate Training (FAT), that alters training between annotated data silos and unannotated data silos. Annotated data silos exploit annotations to learn a reasonable global segmentation model. Meanwhile, unannotated data silos use the global segmentation model as a target model to generate pseudo labels for self-supervised learning. We evaluate the performance of the proposed framework on two naturally partitioned Federated datasets, KiTS19 and FeTS2021, and show its promising performance.