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

翻译：语义分割是从医学（特别是放射学）图像中提取定量信息以辅助诊断过程、临床随访及生成临床研究生物标志物的关键步骤。近年来，机器学习算法已成为完成该任务的主要工具，但其在实际应用中的性能高度依赖于训练数据的全面性。Dafne是首个去中心化协同解决方案，通过利用系统用户的集体知识实现持续演进的深度学习模型。在Dafne工作流中，每次自动分割结果均由用户通过集成界面进行精炼，从而通过联邦增量学习机制持续扩展训练数据池。通过Dafne部署的模型能够随时间推移提升性能，并泛化至训练集未包含的数据类型，因此成为解决真实医学分割场景的可行且实用的方案。