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:从医疗(特别是放射)图像中提取定量信息,以协助诊断过程和临床跟踪,并生成临床研究的生物标志。近年来,机器学习算法已成为这项任务的主要工具。然而,其真实世界的绩效在很大程度上依赖于培训数据的全面性。达夫内是第一个分散化的协作解决方案,它利用系统用户的集体知识,实施不断发展的深层次学习模型。在达夫内工作流程中,每个自动化分离的结果都由用户通过综合接口加以改进,以便利用新信息通过联合递增学习不断扩大培训库。通过达夫内部署的模式能够随着时间的推移改进其绩效,并推广培训组没有看到的数据类型,从而成为现实生活医疗分解任务的可行和实用解决方案。