# Federated Stain Normalization for Computational Pathology

arXiv: https://arxiv.org/abs/2209.14849 · HTML: https://mlsanity.com/paper/2209.14849/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer](https://mlsanity.com/paper/1912.11279/) (2019)
- [FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning](https://mlsanity.com/paper/2102.02514/) (2021)
- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [Siloed Federated Learning for Multi-Centric Histopathology Datasets](https://mlsanity.com/paper/2008.07424/) (2020)
- [Federated Learning for Computational Pathology on Gigapixel Whole Slide Images](https://mlsanity.com/paper/2009.10190/) (2020)
- [Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard](https://mlsanity.com/paper/1808.05883/) (2018)

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning](https://mlsanity.com/paper/2309.00688/) (2023)
- [URCDM: Ultra-Resolution Image Synthesis in Histopathology](https://mlsanity.com/paper/2407.13277/) (2024)
- [PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation](https://mlsanity.com/paper/2505.22522/) (2025)
- [Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity](https://mlsanity.com/paper/2501.04588/) (2025)
- [FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification](https://mlsanity.com/paper/2511.12044/) (2025)
- [ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning](https://mlsanity.com/paper/2508.02414/) (2025)
- [Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology](https://mlsanity.com/paper/2312.01152/) (2023)
- [CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging](https://mlsanity.com/paper/2407.11652/) (2024)
- [FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification](https://mlsanity.com/paper/2609.31150/) (2026)

## 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