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Modality Classification of Medical Images with Distributed Representations Based on Cellular Automata Reservoir Computing
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
2017 (English)Conference paper, (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2017.
National Category
Medical Image Processing
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-61558OAI: oai:DiVA.org:ltu-61558DiVA: diva2:1067142
Conference
2017 IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, 18-21 April 2017
Funder
Swedish Research Council, 2015-04677
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-06-16

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Language
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