mlsanityData to 14 Oct 2026

FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentation

arXiv 2312.12824 ↗First posted 20 Dec 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment. However, it is still a task of great challenges due to the sample imbalance across clients and large data heterogeneity from disparate organs, variable segmentation tasks, and diverse distribution. Thus, we propose a novel FL approach for histopathology nuclei and tissue segmentation, FedSODA, via synthetic-driven cross-assessment operation (SO) and dynamic stratified-layer aggregation (DA). Our SO constructs a cross-assessment strategy to connect clients and mitigate the representation bias under sample imbalance. Our DA utilizes layer-wise interaction and dynamic aggregation to diminish heterogeneity and enhance generalization. The effectiveness of our FedSODA has been evaluated on the most extensive histopathology image segmentation dataset from 7 independent datasets. The code is available at https://github.com/yuanzhang7/FedSODA.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 20 Dec 2023.

Builds on

16 of 26 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  6. 2021Towards Personalized Federated Learningcited by 464
  7. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  8. 2016Gland Segmentation in Colon Histology Images: The GlaS Challenge Contestcited by 203
  9. 2018MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Imagescited by 87
  10. 2023Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentationcited by 84
  11. 2018Methods for Segmentation and Classification of Digital Microscopy Tissue Imagescited by 73
  12. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 64
4 more
  1. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
  2. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  3. 2021CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Imagescited by 51
  4. 2023Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detectioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classificationcited by 1
  2. 2025Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCLcited by 0
  3. 2026FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classificationcited by 0