@inproceedings{scholars8602,
            note = {cited By 18; Conference of 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference Date: 18 April 2017 Through 21 April 2017; Conference Code:128383},
             doi = {10.1109/ISBI.2017.7950697},
           pages = {1053--1056},
            year = {2017},
       publisher = {IEEE Computer Society},
         journal = {Proceedings - International Symposium on Biomedical Imaging},
           title = {Modality classification of medical images with distributed representations based on cellular automata reservoir computing},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023198723&doi=10.1109\%2fISBI.2017.7950697&partnerID=40&md5=94f3e21c26cfd8119076001e82236597},
          author = {Kleyko, D. and Khan, S. and Osipov, E. and Yong, S.-P.},
            isbn = {9781509011711},
            issn = {19457928},
        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. {\^A}{\copyright} 2017 IEEE.},
        keywords = {Classification (of information); Image classification; Image retrieval; Search engines, Classical methods; Classification accuracy; Distributed representation; Incremental learning; Optimization routine; Reservoir Computing; Training data; Training phase, Medical imaging}
}