Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images

Jia Ren Chang*, Min Sheng Wu, Wei Hsiang Yu, Chi Chung Chen, Cheng Kung Yang, Yen-Yu Lin, Chao Yuan Yeh

*Corresponding author for this work

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

19 Scopus citations

Abstract

Computational histopathology studies have shown that stain color variations considerably hamper the performance. Stain color variations indicate the slides exhibit greatly different color appearance due to the diversity of chemical stains, staining procedures, and slide scanners. Previous approaches tend to improve model robustness via data augmentation or stain color normalization. However, they still suffer from generalization to new domains with unseen stain colors. In this study, we address the issue of unseen color domain generalization in histopathology images by encouraging the model to adapt varied stain colors. To this end, we propose a novel data augmentation method, stain mix-up, which incorporates the stain colors of unseen domains into training data. Unlike previous mix-up methods employed in computer vision, the proposed method constructs the combination of stain colors without using any label information, hence enabling unsupervised domain generalization. Extensive experiments are conducted and demonstrate that our method is general enough to different tasks and stain methods, including H&E stains for tumor classification and hematological stains for bone marrow cell instance segmentation. The results validate that the proposed stain mix-up can significantly improves the performance on the unseen domains.

Original languageAmerican English
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages117-126
Number of pages10
ISBN (Print)9783030871987
DOIs
StatePublished - Sep 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12903 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • Domain generalization
  • Mix-up
  • Stain color

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