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dc.contributor.author
Pati, Pushpak
dc.contributor.author
Jaume, Guillaume
dc.contributor.author
Foncubierta-Rodríguez, Antonio
dc.contributor.author
Feroce, Florinda
dc.contributor.author
Anniciello, Anna Maria
dc.contributor.author
Scognamiglio, Giosuè
dc.contributor.author
Brancati, Nadia
dc.contributor.author
Fiche, Maryse
dc.contributor.author
Dubruc, Estelle
dc.contributor.author
Riccio, Daniel
dc.contributor.author
Di Bonito, Maurizio
dc.contributor.author
De Pietro, Giuseppe
dc.contributor.author
Botti, Gerardo
dc.contributor.author
Thiran, Jean-Philippe
dc.contributor.author
Frucci, Maria
dc.contributor.author
Goksel, Orcun
dc.contributor.author
Gabrani, Maria
dc.date.accessioned
2021-12-03T18:29:28Z
dc.date.available
2021-11-27T04:09:41Z
dc.date.available
2021-12-03T18:29:28Z
dc.date.issued
2022-01
dc.identifier.issn
1361-8415
dc.identifier.issn
1361-8423
dc.identifier.other
10.1016/j.media.2021.102264
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/517292
dc.identifier.doi
10.3929/ethz-b-000517292
dc.description.abstract
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Digital pathology
en_US
dc.subject
Breast cancer classification
en_US
dc.subject
Cell graph representation
en_US
dc.subject
Tissue graph representation
en_US
dc.subject
Hierarchical tissue representation
en_US
dc.subject
Hierarchical graph neural network
en_US
dc.subject
Breast cancer dataset
en_US
dc.title
Hierarchical graph representations in digital pathology
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2021-10-27
ethz.journal.title
Medical Image Analysis
ethz.journal.volume
75
en_US
ethz.journal.abbreviated
Med Image Anal
ethz.pages.start
102264
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09528 - Göksel, Orçun (ehemalig) / Göksel, Orçun (former)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09528 - Göksel, Orçun (ehemalig) / Göksel, Orçun (former)
ethz.relation.cites
10.3929/ethz-b-000548471
ethz.date.deposited
2021-11-27T04:10:29Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-12-03T18:29:36Z
ethz.rosetta.lastUpdated
2023-02-06T23:23:41Z
ethz.rosetta.versionExported
true
ethz.COinS
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