Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images

Andrew Moyes, Kun Zhang*, Ming Ji, Huiyu Zhou, Danny Crookes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stain separation is an important pre-processing technique used to aid automated analysis of histopathology images. In this paper, we propose a novel, unsupervised deep learning method for stain separation (Hematoxylin and Eosin). This approach is inspired by Non-Negative Matrix Factorisation (NMF) and decomposes an input image into a stain colour matrix and a stain concentration matrix. In contrast to existing approaches, our method predicts stain colour matrices at the pixel level rather than the image level, thus enabling implicit modelling of tissue-dependant interactions between stains. We demonstrate an 8.81% reduction in mean-squared error on a stain separation task measuring the similarity between predicted and actual hematoxylin images from a publicly available dataset of digitised tissue images. We also present a novel approach to artifact detection in histological images based on a constrained generative adversarial network which we demonstrate is able to detect a variety of artifact types without the use of labels.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings
EditorsBartlomiej W. Papiez, Ana I.L. Namburete, Mohammad Yaqub, J. Alison Noble, Mohammad Yaqub
PublisherSpringer
Pages221-234
Number of pages14
ISBN (Print)9783030527907
DOIs
Publication statusPublished - 08 Jul 2020
Event24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020 - Oxford, United Kingdom
Duration: 15 Jul 202017 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1248 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020
CountryUnited Kingdom
CityOxford
Period15/07/202017/07/2020

Bibliographical note

Funding Information:
This work was financially supported by Invest NI, the Natural Science Foundation of Jiangsu Province, China under Grant No. BK20170443 and the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 17KJB520030.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Artifact detection
  • Stain separation
  • Unsupervised

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Best Paper

    Moyes, A. (Recipient), 2020

    Prize: Prize (including medals and awards)

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