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@article{44568,
   author = {Hecht, Helge and Mhd Hasan, Sarhan and Popovici, Vlad and Popovici, Vlad and Sarhan, Hasan},
   article_location = {Basel},
   article_number = {18},
   doi = {http://dx.doi.org/10.3390/app10186427},
   keywords = {digital pathology; image registration; deep learning; disentangled autoencoder},
   language = {eng},
   issn = {2076-3417},
   journal = {APPLIED SCIENCES},
   title = {Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis},
   url = {https://doi.org/10.3390/app10186427},
   volume = {10},
   year = {2020}
}

TY  - JOUR
ID  - 44568
AU  - Hecht, Helge - Mhd Hasan, Sarhan - Popovici, Vlad - Popovici, Vlad - Sarhan, Hasan
PY  - 2020
TI  - Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
JF  - APPLIED SCIENCES
VL  - 10
IS  - 18
SP  - 1-14
EP  - 1-14
PB  - MDPI
SN  - 2076-3417
KW  - digital pathology
KW  - image registration
KW  - deep learning
KW  - disentangled autoencoder
UR  - https://doi.org/10.3390/app10186427
N2  - 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.
ER  - 

HECHT, Helge, Sarhan MHD HASAN a Vlad POPOVICI. Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis. \textit{APPLIED SCIENCES}. Basel: MDPI, 2020, roč.~10, č.~18, s.~1-14. ISSN~2076-3417. Dostupné z: https://dx.doi.org/10.3390/app10186427.