Presented at DeCaF (Distributed and Collaborative Federated Learning) 2025, MICCAI.

Work by Simone Bendazzoli, Mehdi Astaraki, Antonios Tzortzakakis, Andréas Abrahamsson, Björn Engelbrekt Wahlin, Sofia Brunori, Maria Holstensson, and Rodrigo Moreno.

Abstract

While nnU-Net’s success as a state-of-the-art tool for medical image segmentation has driven its adoption as a baseline, its limited portability and lack of clinical integration have limited broader deployment in real-world healthcare workflows. The MONet Bundle addresses these challenges by extending nnU-Net within the MONAI ecosystem, providing a modular benchmarking tool for federated learning that is directly compatible with downstream clinical operations such as model deployment, active learning, and DICOM-based PACS integration.

MONet enables federated training across distributed clinical datasets while maintaining standardised preprocessing and harmonised workflows. Its flexibility is validated on two representative segmentation tasks: lymphoma lesion segmentation in PET-CT and brain-tumour segmentation from the BraTS challenge.