Pseudo-Global Based Sequential Contribution Estimation for Federated Semi-Supervised Medical Image Segmentation.

Abstract

Federated semi-supervised learning (FSSL) for medical image segmentation has been extensively studied in recent years. Due to the requirement of specialized knowledge and equipment for annotating medical data, only a very limited number of medical institutions have a small amount of labeled data. However, existing federated semi-supervised segmentation methods primarily focus on fully supervised clients to improve average performance and often overlook the contributions of unsupervised clients. To effectively leverage unsupervised clients and extract more valuable information, we propose pseudo-global based sequential contribution estimation for federated semi-supervised segmentation, abbreviated as FedPSC. FedPSC first estimates the performance contributions of all clients using the difference in validation performance, and then builds a pseudo-global model based on this. Sequentially, the pseudo-global model and the union generated by the exclusion effect are used to estimate the gradient contribution of the client, thereby establishing a relationship between the two contributions. Besides, we also introduce a gradient direction exponential moving average method, which aims to train client models by integrating both global general knowledge and local personalized insights. Experimental results on two commonly used datasets confirm the effectiveness of our proposed method. Additional experiments and analysis are also provided to give in-depth understanding of FedPSC.