Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization

Bibtex entry :

@techreport { graziani:arxiv2022,
    title = { Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization },
    author = { Mara Graziani and Sebastian Otalora and Stephane Marchand-Maillet and Henning Muller and Vincent Andrearczyk },
    year = { 2022 },
    eprint = { 2008.01478 },
    volume = { abs/2008.01478 },
    journal = { CoRR },
    url = { https://arxiv.org/abs/2008.01478 },
    archiveprefix = { arXiv },
    primaryclass = { cs.CV },
}
--

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