Using non-lexical features to identify effective indexing terms for biomedical illustrations
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Automatic Detection of Arrow Annotation Overlays in Biomedical Images
International Journal of Healthcare Information Systems and Informatics
Bag---of---Colors for biomedical document image classification
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Medical (visual) information retrieval
PROMISE'12 Proceedings of the 2012 international conference on Information Retrieval Meets Information Visualization
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
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Essential information is often conveyed in illustrations in biomedical publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision is frequently based solely on a short bibliographic reference. We seek to automatically augment these references with images from the article that may assist in finding evidence. The feasibility of automatically classifying images by usefulness (utility) in finding evidence was explored using supervised machine learning. We selected 2004 - - 2005 issues of the British Journal of Oral and Maxillofacial Surgery, manually annotating 743 images by utility and modality (radiological, photo, etc.) Image data, figure captions, and paragraphs surrounding figure discussions in text were used in classification. Automatic image classification achieved 84.3% accuracy using image captions for modality and 76.6% accuracy combining captions and image data for utility. Our results indicate that automatic augmentation of bibliographic references with relevant images is feasible.