Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F20%3A00114485" target="_blank" >RIV/00216224:14310/20:00114485 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/app10186427" target="_blank" >https://doi.org/10.3390/app10186427</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app10186427" target="_blank" >10.3390/app10186427</a>
Alternative languages
Result language
angličtina
Original language name
Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences
ISSN
2076-3417
e-ISSN
2076-3417
Volume of the periodical
10
Issue of the periodical within the volume
18
Country of publishing house
CH - SWITZERLAND
Number of pages
14
Pages from-to
1-14
UT code for WoS article
000580750500001
EID of the result in the Scopus database
2-s2.0-85092060091