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MONet-FL: Extending nnU-Net with MONAI for Clinical Federated Learning
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DOI：10.1007/978-3-032-05663-4_10.png)
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Abstract
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The widespread success of nnU-Net as a state-of-the-art tool for medical image segmentation has driven its adoption as a baseline, but its limited portability and lack of clinical integration have limited broader deployment in real-world healthcare workflows. To address these challenges, we present the MONet Bundle, extending nnU-Net within the MONAI ecosystem, providing a modular benchmarking tool for Federated Learning (FL) that is directly compatible with downstream clinical operations such as model deployment, active learning, and DICOMbased PACS integration. MONet enables federated training across distributed clinical datasets while maintaining standardized preprocessing and harmonized workflows. Its flexibility is validated on two representative segmentation tasks: lymphoma lesion segmentation in PET-CT and brain tumor segmentation from the BraTS challenge. In both settings, MONet's federated models consistently outperformed cross-site baselines and approached, or in some cases outperformed, the performance of centralized task-fusion models with minimal user intervention. The code is available at https://github.com/SimoneBendazzoli93/MONet- Bundle.
Keywords:
nnU-Net
MONAI
Federated Learning
Medical Image Segmentation
Clinical Integration
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