ArXiv Preprint
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.
Francesco Santini, Jakob Wasserthal, Abramo Agosti, Xeni Deligianni, Kevin R. Keene, Hermien E. Kan, Stefan Sommer, Christoph Stuprich, Fengdan Wang, Claudia Weidensteiner, Giulia Manco, Valentina Mazzoli, Arjun Desai, Anna Pichiecchio
2023-02-13
