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

conference contribution
posted on 2020-05-05, 13:17 authored by A Moyes, K Zhang, M Ji, Huiyu Zhou, D Crookes
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 Factorization (NMF) and decomposes an input image into a stain color matrix and a stain concentration matrix.In contrast to existing approaches, our method predicts stain color 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 digitized 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 with-out the use of labels.

History

Citation

Conference on Medical Image Understanding and Analysis, In Press

Version

  • AM (Accepted Manuscript)

Published in

Conference on Medical Image Understanding and Analysis

Acceptance date

2020-05-04

Copyright date

2020

Publisher version

TBA

Language

en

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