Federated nnU-Net for Privacy-Preserving Medical Image Segmentation

• URL: http://arxiv.org/abs/2503.02549v1
• Date: Tue, 04 Mar 2025 12:20:06 GMT
• Title: Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
• Authors: Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla, Lidia Garrucho, Dimitri A. Kessler, Esmeralda Ruiz Pujadas, Oliver Díaz, Maciej Bobowicz, Katarzyna Gwoździewicz, Xavier Bargalló, Paulius Jaruševičius, Kaisar Kushibar, Karim Lekadir,
• Abstract summary: Federated learning is one of the approaches to train a segmentation model in a decentralized manner that helps preserve patient privacy.<n>We introduce two novel federated learning methods to the nnU-Net framework - Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg)<n>To further promote research and deployment of decentralized training in privacy constrained institutions, we make our plug-n-play framework public.
• Score: 0.9297517753400492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the data collected from hospitals are stored in one center and used to train the nnU-Net. This centralized approach has various limitations, such as leakage of sensitive patient information and violation of patient privacy. Federated learning is one of the approaches to train a segmentation model in a decentralized manner that helps preserve patient privacy. In this paper, we propose FednnU-Net, a federated learning extension of nnU-Net. We introduce two novel federated learning methods to the nnU-Net framework - Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg) - and experimentally show their consistent performance for breast, cardiac and fetal segmentation using 6 datasets representing samples from 18 institutions. Additionally, to further promote research and deployment of decentralized training in privacy constrained institutions, we make our plug-n-play framework public. The source-code is available at https://github.com/faildeny/FednnUNet .

Related papers

• Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare [0.0]
  In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning. We propose a federated learning framework based on quantum tensor networks, which leverages the principles of many-body quantum physics. Experiments on popular medical image datasets show that the federated quantum tensor network model achieved a mean receiver-operator characteristic area under the curve (ROC-AUC) between 0.91-0.98.
  arXiv  Detail & Related papers  (2024-05-13T13:32:02Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Federated Multi-organ Segmentation with Inconsistent Labels [14.096407787470701]
  Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. This work tackles the problem by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation.
  arXiv  Detail & Related papers  (2022-06-14T20:34:40Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation [66.44449514373746]
  Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years. There can be a gap between the model trained from FL and one from centralized training. We propose a novel training framework FedSM to avoid client issue and successfully close the drift gap.
  arXiv  Detail & Related papers  (2022-03-18T19:50:07Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [63.43592895652803]
  Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks.
  arXiv  Detail & Related papers  (2021-03-10T13:05:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.