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Open AccessArticle

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

1
RECETOX, Masaryk University, 62500 Brno, Czech Republic
2
Chair for Computer Aided Medical Procedures, Technical University of Munich, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(18), 6427; https://doi.org/10.3390/app10186427
Received: 3 July 2020 / Revised: 8 September 2020 / Accepted: 11 September 2020 / Published: 15 September 2020
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing)
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. View Full-Text
Keywords: digital pathology; image registration; deep learning; disentangled autoencoder digital pathology; image registration; deep learning; disentangled autoencoder
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Hecht, H.; Sarhan, M.H.; Popovici, V. Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis. Appl. Sci. 2020, 10, 6427.

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