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primary topic: Modality classification of medical images with distributed representations based on cellular automata reservoir computing

Modality classification of medical images with distributed representations based on cellular automata reservoir computing
type: AcademicArticle
type: Article
abstract: Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset (83 vs. 84). The major positive property of the proposed method is that it does not require any optimization routine during the training phase and naturally allows for incremental learning upon the availability of new training data. © 2017 IEEE.
date: 2017
is part of: Proceedings - International Symposium on Biomedical Imaging
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

IEEE Computer Society
type: Organization

Proceedings - International Symposium on Biomedical Imaging
type: Collection
issn: 19457928

E. Osipov
type: Person
given name: E.
family name: Osipov

D. Kleyko
type: Person
given name: D.
family name: Kleyko

S.-P. Yong
type: Person
given name: S.-P.
family name: Yong

S. Khan
type: Person
given name: S.
family name: Khan

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primary topic: Modality classification of medical images with distributed representations based on cellular automata reservoir computing