BUDINSKÁ, Eva, Fred BOSMAN and Vlad POPOVICI. Experiments in Molecular Subtype Recognition Based on Histopathology Images. In 2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI). NEW YORK: IEEE. p. 1168-1172. ISBN 978-1-4799-2350-2. doi:10.1109/ISBI.2016.7493474. 2016.
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Basic information
Original name Experiments in Molecular Subtype Recognition Based on Histopathology Images
Authors BUDINSKÁ, Eva (703 Slovakia, guarantor, belonging to the institution), Fred BOSMAN (756 Switzerland) and Vlad POPOVICI (642 Romania, belonging to the institution).
Edition NEW YORK, 2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), p. 1168-1172, 5 pp. 2016.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14310/16:00093777
Organization unit Faculty of Science
ISBN 978-1-4799-2350-2
ISSN 1945-7928
Doi http://dx.doi.org/10.1109/ISBI.2016.7493474
UT WoS 000386377400276
Keywords in English colon cancer; histopathology imaging; classification; gastrointestinal tract
Tags AKR, podil, rivok
Tags International impact, Reviewed
Changed by Changed by: Ing. Andrea Mikešková, učo 137293. Changed: 26/4/2017 23:13.
Abstract
Molecular subtypes have been recently derived for various types of cancer, in an attempt to characterize the inter-tumoral heterogeneity. In this work we explore the possibility of constructing predictors for molecular subtypes based on histopathology images. For this, we introduce a novel 2-level bag-of-features method and we apply it to a collection of colorectal cancer samples. The resulting image features capture some relevant tumor morphology patterns and led to a classifier performing similarly to one constructed from features annotated by an expert pathologist. The significance of our results extends beyond subtype prediction since they demonstrate a possible approach to multimodal (histopathology and molecular) data mining and biomarker identification.
Links
4SGA8736, interní kód MUName: Computational framework for joint analysis of histopathology images and gene expression data (Acronym: HIGEX)
Investor: South-Moravian Region, Incoming grants
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