Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
This work addresses privacy and data scarcity challenges in medical imaging segmentation, though it is incremental as it adapts an existing method to a new scenario.
The paper tackles the problem of medical image segmentation in data-scarce, privacy-sensitive settings by adapting a self-supervised few-shot segmentation framework to federated learning across different modalities like MR and CT, achieving performance comparable to or better than a baseline federated version on held-out data.
Decentralized federated learning enables learning of data representations from multiple sources without compromising the privacy of the clients. In applications like medical image segmentation, where obtaining a large annotated dataset from a single source is a distressing problem, federated self-supervised learning can provide some solace. In this work, we push the limits further by exploring a federated self-supervised one-shot segmentation task representing a more data-scarce scenario. We adopt a pre-existing self-supervised few-shot segmentation framework CoWPro and adapt it to the federated learning scenario. To the best of our knowledge, this work is the first to attempt a self-supervised few-shot segmentation task in the federated learning domain. Moreover, we consider the clients to be constituted of data from different modalities and imaging techniques like MR or CT, which makes the problem even harder. Additionally, we reinforce and improve the baseline CoWPro method using a fused dice loss which shows considerable improvement in performance over the baseline CoWPro. Finally, we evaluate this novel framework on a completely unseen held-out part of the local client dataset. We observe that the proposed framework can achieve performance at par or better than the FedAvg version of the CoWPro framework on the held-out validation dataset.