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HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM

HEp-2 Specimen Classification Using Multi-resolution Local Patterns and SVM

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Original languageEnglish
Title of host publicationProceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014
PublisherIEEE Computer Society
Pages41-44
Number of pages4
ISBN (Print)9781479942527
DOIs
StatePublished - 2014
Event13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm, Sweden

Workshop

Workshop13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images
CountrySweden
CityStockholm
Period24/08/1424/08/14
Internet address

Abstract

A pattern recognition system was developed to classify immunofluorescence images of HEp-2 specimens into seven classes: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. Root-SIFT features together with multi-resolution local patterns were used to capture local shape and texture information. Sparse coding with max-pooling was applied to get an image representation from these local features. Specimens were classified using a linear support vector machine. Leave-one-specimen-out experiments on the I3A Contest Task 2 data set predicted a mean class accuracy of 89.9%.

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