Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency

arXiv:2403.12695v1 Announce Type: cross
Abstract: Medical image segmentation plays a vital role in clinic disease diagnosis and medical image analysis. However, labeling medical images for segmentation task is tough due to the indispensable domain expertise of radiologists. Furthermore, considering the privacy and sensitivity of medical images, it is impractical to build a centralized segmentation dataset from different medical institutions. Federated learning aims to train a shared model of isolated clients without local data exchange which aligns well with the scarcity …

abstract analysis arxiv client cs.cv cs.lg diagnosis disease disease diagnosis domain eess.iv expertise however image images labeling medical privacy role segmentation semi-supervised semi-supervised learning sensitivity supervised learning type vital