Subnetwork-based Federated Few-shot semantic seg-mentation of Organ Images
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Abstract:
Federated learning, as a distributed learning paradigm, allows multiple medical institutions to collaborate on learning without the need to centralize all client data. However, existing methods pay little attention to more challenging medical image semantic segmentation tasks, especially in the scenario of uneven data category distribution in fed-erated few-shot learning. In this context, we propose a subnetwork-based federated few-shot medical image seg-mentation method. Firstly, individual clients train using local training samples and then upload local model gradients to the server. The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters. Through this method, we are able to learn the similarities between different clients to address data heterogeneity issues. In addition, to enhance the communication efficiency between clients and the server, we have also designed a personalized layer aggregation strategy, which only transmits partial layer model parameters during the communication process to improve com-munication efficiency. Finally, we conducted experiments on ABD-MRI and ABD-CT datasets to demonstrate the effectiveness of our method.
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Project Supported:
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
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