Information fusion for medical data : early, late, and deep fusion methods for multimodal data

Domingues, Inés (IPO Porto Research Centre (CI-IPOP), Porto, Portugal) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Ortiz, Andres (Universidad de Málaga, Spain) ; Dasarathy, Belur V. (Consultant - Decision Systems & Information Fusion Technologies) ; Abreu, Pedro H. (Consultant - Decision Systems & Information Fusion Technologies) ; Calhoun, Vince D. (Consultant - Decision Systems & Information Fusion Technologies)

The papers in this special section examine important current topics on multimodal data fusion in the medical context. All clinical data, including genomic and proteomic, play a role in the diagnosis and in particular in the treatment planning and follow-up. This is true for all types of data analyses whether in classification, regression, retrieval, clustering, or other. The interaction between several types of information is not always well understood. Experienced clinicians automatically and even unconsciously add multiple sources of information into their decision process, but machine learning tools often concentrate on single information sources. This special issue presents five examples where several data sources are fused. The papers give several examples of fusion techniques and also the results obtained in quite different application scenarios.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2020-01
Pagination:
3 p.
Published in:
IEEE Journal of biomedical and health informatics
Numeration (vol. no.):
2020, vol. 24, no. 1, pp. 14-16
DOI:
ISSN:
2168-2194
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-11-18, last modified 2021-01-25

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