FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
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Abstract (arXiv)
Federated learning has emerged as a compelling paradigm for medical image segmentation, particularly in light of increasing privacy concerns. However, most of the existing research relies on relatively stringent assumptions regarding the uniformity and completeness of annotations across clients. Contrary to this, this paper highlights a prevalent challenge in medical practice: incomplete annotations. Such annotations can introduce incorrectly labeled pixels, potentially undermining the performance of neural networks in supervised learning. To tackle this issue, we introduce a novel solution, named FedIA. Our insight is to conceptualize incomplete annotations as noisy data (i.e., low-quality data), with a focus on mitigating their adverse effects. We begin by evaluating the completeness of annotations at the client level using a designed indicator. Subsequently, we enhance the influence of clients with more comprehensive annotations and implement corrections for incomplete ones, thereby ensuring that models are trained on accurate data. Our method's effectiveness is validated through its superior performance on two extensively used medical image segmentation datasets, outperforming existing solutions. The code is available at https://github.com/HUSTxyy/FedIA.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
15 of 23 references.
References · 15
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 692
- 2020Early-Learning Regularization Prevents Memorization of Noisy Labelscited by 416
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
- 2021Adaptive Early-Learning Correction for Segmentation from Noisy Annotationscited by 62
- 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
- 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 52
- 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 40
- 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
- 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
3 more
- 2022FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classificationcited by 18
- 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
- 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.