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Proceedings Paper

Text- and content-based biomedical image modality classification
Author(s): Daekeun You; Md Mahmudur Rahman; Sameer Antani; Dina Demner-Fushman; George R. Thoma
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Paper Abstract

Image modality classification is an important task toward achieving high performance in biomedical image and article retrieval. Imaging modality captures information about its appearance and use. Examples include X-ray, MRI, Histopathology, Ultrasound, etc. Modality classification reduces the search space in image retrieval. We have developed and evaluated several modality classification methods using visual and textual features extracted from images and text data, such as figure captions, article citations, and MeSH®. Our hierarchical classification method using multimodal (mixed textual and visual) and several class-specific features achieved the highest classification accuracy of 63.2%. The performance was among the best in ImageCLEF2012 evaluation.

Paper Details

Date Published: 29 March 2013
PDF: 8 pages
Proc. SPIE 8674, Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 86740L (29 March 2013); doi: 10.1117/12.2007932
Show Author Affiliations
Daekeun You, National Library of Medicine, National Institute of Health (United States)
Md Mahmudur Rahman, National Library of Medicine, National Institute of Health (United States)
Sameer Antani, National Library of Medicine, National Institute of Health (United States)
Dina Demner-Fushman, National Library of Medicine, National Institute of Health (United States)
George R. Thoma, National Library of Medicine, National Institute of Health (United States)


Published in SPIE Proceedings Vol. 8674:
Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications
Maria Y. Law; William W. Boonn, Editor(s)

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