H{\&}E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin {\&} Eosin regression

Bibtex entry :

@inproceedings { marini:iccvw2021,
    author = { Niccol{\`{o}} Marini and Manfredo Atzori and Sebastian Ot{\'{a}}lora and Stephane Marchand-Maillet and Henning M{\"{u}}ller },
    title = { H{\&}E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin {\&} Eosin regression },
    booktitle = { {IEEE/CVF} International Conference on Computer Vision Workshops, {ICCVW} 2021, Montreal, BC, Canada, October 11-17, 2021 },
    pages = { 601--610 },
    publisher = { {IEEE} },
    year = { 2021 },
    url = { https://doi.org/10.1109/ICCVW54120.2021.00073 },
    doi = { 10.1109/ICCVW54120.2021.00073 },
}
--

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion