… The dice score for segmenting mediastinal neoplasms of CAIMEN was 0·765 (0·738–0·792).
The mediastinal neoplasm classification top-1 and top-3 accuracy of CAIMEN were 0·523 (0·…

… ” which is a federated learning model for brain tumor segmentation that is based … segmentation
DeepLabv3+ model and the classical model U-Net allocated to semantic segmentation …

… We validate the effectiveness of MH-pFLGB in medical image segmentation tasks. Table
3 presents the results of previous federated learning frameworks in the segmentation task, …

… Through empirical studies, we find that traditional methods in partially supervised segmentation
and federated learning often struggle with class collision when combined. Our extensive …

… segmentation targets. Different from existing works, we investigate a novel federated learning
… in histopathology nuclei and tissue segmentation tasks, which dynamically aggregates …

… challenges in federated learning. 1) … federated learning: A segmented gossip approach,”
arXiv preprint arXiv:1908.07782, 2019. [9] J. Jiang and L. Hu, “Decentralised federated learning …

… , where model-learning leverages all available data without … We show that federated learning
among 10 institutions … on data from institutions outside the federation. We further investigate …

… First, Section 3.1 explores the evolution of studies on federated learning applied to healthcare,
… The second is medical image segmentation and classification of diagnostic prediction of …

… to leverage unlabeled data for federated learning. In the … of federated learning for COVID
region segmentation are … of federated semi-supervised learning in the segmentation task …

… Recent developments in federated learning (FL) have made it possible to train complex
machine-learned models in a distributed manner and have become an active research domain, …