What If Kidney Tumor Segmentation Challenge (KiTS19) Never Happened

Abstract

Federated Learning (FL) is an efficient distributed machine learning algorithm that promises to reduce data migration costs to a centralized repository, alleviate regulatory data restrictions and maintain data privacy. However, it suffers from data heterogeneity, i.e., data distribution across silos is often non-identical and independent (non-iid), making optimization difficult. Further, a significant but rarely studied challenge in FL is the lack of annotated data for training. This challenge is more pronounced in the medical field since data annotations require precision and are highly labor-intensive and time-consuming. That is why a few sites have minimal or no annotated data, which wastes valuable data resources for ML training. In this work, we investigate these two challenges of Federated Learning on a publicly available realistic federated medical dataset, KiTS19. First, we explore Federated Learning for Tumor Segmentation task on the Federated version of the KiTS19 dataset for the first time. We show that FL can maintain 96% of model accuracy compared to the centralized model accuracy with ten institution collaboration. In addition, we investigate the benefits of transfer learning to address the challenge of data heterogeneity and show that 5% accuracy improvement is achieved by using a pre-trained model in FL. Moreover, we propose a Federated semi-supervised learning (FSSL) framework to address the challenge of the lack of annotations at some silos. We show that unlabelled silos add 11% to the model’s efficiency compared with the model trained on labeled silos alone.

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