Exploring use of Images in Clinical Articles for Decision Support in Evidence-Based Medicine
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@MISC{Antani_exploringuse,
author = {Sameer Antani and Dina Demner-fushman and Jiang Li A and Balaji V. Srinivasan B and George R. Thoma},
title = {Exploring use of Images in Clinical Articles for Decision Support in Evidence-Based Medicine},
year = {}
}
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Abstract
Essential information is often conveyed pictorially (images, illustrations, graphs, charts, etc.) 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. In a previous study, the feasibility of automatically classifying images by usefulness (utility) in finding evidence was explored using supervised machine learning and achieved 84.3 % accuracy using image captions for modality and 76.6 % accuracy combining captions and image data for utility on 743 images from articles over 2 years from a clinical journal. Our results indicated that automatic augmentation of bibliographic references with relevant images was feasible. Other research in this area has determined improved user experience by showing images in addition to the short bibliographic reference. Multi-panel images used in our study had to be manually pre-processed for image analysis, however. Additionally, all image-text on figures was ignored. In this article, we report on developed methods for automatic multi-panel image segmentation using not only image features, but also clues from text analysis applied to figure captions. In initial experiments on 516 figure images we obtained 95.54 % accuracy in correctly identifying and segmenting the sub-images. The errors were flagged as disagreements with automatic parsing of figure caption text allowing for supervised segmentation. For localizing text and symbols, on a randomly selected test set of 100 single panel images our methods reported, on the average, precision and recall of 78.42 % and 89.38%, respectively, with an accuracy of 72.02%.
Keyphrases
decision support clinical article evidence-based medicine short bibliographic reference image data supervised machine learning biomedical publication automatic multi-panel image segmentation test set previous study user experience automatic parsing bibliographic reference clinical journal clinician decision image caption full text supervised segmentation developed method clinical decision essential information figure caption text single panel initial experiment image feature relevant image multi-panel image image analysis automatic augmentation text analysis
