Semi-supervised adversarial learning for stain normalization in histopathology images

Cong Cong, Sidong Liu, Antonio Di Ieva, Maurice Pagnucco, Shlomo Berkovsky, Yang Song*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Hematoxylin and Eosin (H&E) stained histopathology images provide important clues for diagnostic and prognostic assessment of diseases. However, similar tissues can be stained with inconsistent colours which significantly hinder the diagnostic process and training of deep learning models. Various Generative Adversarial Network (GAN) based stain normalisation methods have thus been proposed as a preprocessing step for the downstream classification or detection tasks. However, most of these methods are based on either unsupervised learning which suffers from large discrepancy between domains or supervised learning which requires a target domain and only utilises the target domain images. In this work, we propose to leverage Semi-supervised Learning with GAN to incorporate the source domain images in the learning of stain normalisation without requiring their corresponding ground truth data. Our approach achieves highly effective performance on two classification tasks for brain and breast cancers.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention - MICCAI 2021
Subtitle of host publication24th International Conference. Proceedings, Part VIII
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Place of PublicationSwitzerland
PublisherSpringer, Springer Nature
Number of pages11
ISBN (Electronic)9783030872373
ISBN (Print)9783030872366
Publication statusPublished - 21 Sep 2021
Event24th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2021 - Strasbourg, France
Duration: 27 Sep 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Medical Image Computing and Computer-Assisted Intervention


  • Semi-supervised learning
  • Stain normalisation
  • Conditional generative adversarial networks


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