mlsanityData to 7 Oct 2026

Federated Stain Normalization for Computational Pathology

arXiv 2209.14849 ↗First posted 29 Sep 2022eess.IV cs.CVcited by 11

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Abstract (arXiv)

Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since different laboratories have different staining styles. As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology. We construct a heterogenic multi-institutional dataset based on the PESO segmentation dataset and improve the IOU by 42% compared to existing federated learning algorithms. An implementation of BottleGAN is available at https://github.com/MECLabTUDA/BottleGAN

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

23 of 30 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  8. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  9. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  10. 2020Adaptive Federated Optimizationcited by 1,036
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  12. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
11 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  2. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  3. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  4. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  5. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  6. 2019Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfercited by 91
  7. 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
  8. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  9. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  10. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  11. 2018Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standardcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 4; 2026: 1.

11 papers.

  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  3. 2023Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learningcited by 5
  4. 2024URCDM: Ultra-Resolution Image Synthesis in Histopathologycited by 4
  5. 2025Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuitycited by 2
  6. 2025PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentationcited by 2
  7. 2025FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classificationcited by 2
  8. 2025ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learningcited by 1
  9. 2023Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathologycited by 0
  10. 2024CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imagingcited by 0
  11. 2026FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classificationcited by 0