Whole-brain radiomics for clustered federated personalization in brain tumor segmentation
Not on the map. Why?
Abstract (arXiv)
Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity between participating institutions' local datasets, incurring convergence slowdown as well as potential accuracy loss compared to classical training. To mitigate this effect, federated personalization emerged as the federated optimization of one model per institution. We propose a novel personalization algorithm tailored to the feature shift induced by the usage of different scanners and acquisition parameters by different institutions. This method is the first to account for both inter and intra-institution feature shift (multiple scanners used in a single institution). It is based on the computation, within each centre, of a series of radiomic features capturing the global texture of each 3D image volume, followed by a clustering analysis pooling all feature vectors transferred from the local institutions to the central server. Each computed clustered decentralized dataset (potentially including data from different institutions) then serves to finetune a global model obtained through classical federated learning. We validate our approach on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022). Our code is available at (https://github.com/MatthisManthe/radiomics_CFFL).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 17 Oct 2023.
Builds on
19 of 29 references.
References · 19
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
- 2020Federated Learning with Matched Averagingcited by 564
- 2019Federated Learning with Personalization Layerscited by 541
- 2020An Efficient Framework for Clustered Federated Learningcited by 511
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
- 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
- 2021Personalized Federated Learning using Hypernetworkscited by 212
- 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
- 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
7 more
- 2020Salvaging Federated Learning by Local Adaptationcited by 142
- 2022Federated Learning with Partial Model Personalizationcited by 114
- 2021What Do We Mean by Generalization in Federated Learning?cited by 62
- 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
- 2021The Federated Tumor Segmentation (FeTS) Challengecited by 50
- 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
- 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
Built on by
Citing papers per year: 2024: 1.
1 paper.