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Semantic segmentation is a crucial step to extract quantitative informationfrom medical (and, specifically, radiological) images to aid the diagnosticprocess, clinical follow-up. and to generate biomarkers for clinical research.In recent years, machine learning algorithms have become the primary tool forthis task. However, its real-world performance is heavily reliant on thecomprehensiveness of training data. Dafne is the first decentralized,collaborative solution that implements continuously evolving deep learningmodels exploiting the collective knowledge of the users of the system. In theDafne workflow, the result of each automated segmentation is refined by theuser through an integrated interface, so that the new information is used tocontinuously expand the training pool via federated incremental learning. Themodels deployed through Dafne are able to improve their performance over timeand to generalize to data types not seen in the training sets, thus becoming aviable and practical solution for real-life medical segmentation tasks.

Deep Anatomical Federated Network (Dafne): an open client/server  framework for the continuous collaborative improvement of deep-learning-based  medical image segmentation