Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

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Authors

HECHT Helge POPOVICI Vlad MHD HASAN Sarhan SARHAN Hasan POPOVICI Vlad

Year of publication 2020
Type Article in Periodical
Magazine / Source Applied Sciences
MU Faculty or unit

Faculty of Science

Citation
Web https://doi.org/10.3390/app10186427
Doi http://dx.doi.org/10.3390/app10186427
Keywords digital pathology; image registration; deep learning; disentangled autoencoder
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Description A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization.
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