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

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.

A kinetic approach to consensus-based segmentation of biomedical images

In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static characteristic of the system, i.e. the gray level of each pixel. From the introduced microscopic model we derive a kinetic formulation of the model. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach that can be obtained in the quasi-invariant scaling. We exploit the computational efficiency of direct simulation Monte Carlo methods for the obtained Boltzmann-type description of the problem for parameter identification tasks. Based on a suitable loss function measuring the distance between the ground truth segmentation mask and the evaluated mask, we minimize the introduced segmentation metric for a relevant set of 2D gray-scale images. Applications to biomedical segmentation concentrate on different imaging research contexts.