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

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HECHT Helge POPOVICI Vlad MHD HASAN Sarhan SARHAN Hasan POPOVICI Vlad

Rok publikování 2020
Druh Článek v odborném periodiku
Časopis / Zdroj Applied Sciences
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://doi.org/10.3390/app10186427
Doi http://dx.doi.org/10.3390/app10186427
Klíčová slova digital pathology; image registration; deep learning; disentangled autoencoder
Přiložené soubory
Popis 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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