Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images
| Autor: | Kun Zhang, Danny Crookes, Ming Ji, Huiyu Zhou, Andrew D. Moyes |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030527907 MIUA |
| DOI: | 10.1007/978-3-030-52791-4_18 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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