Human-Centered Perceptual and Conceptual Classification of Biomedical Images

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DESCRIPTION (provided by applicant): Biomedical images are ever increasing in quantity and importance yet effective computing solutions for managing images and understanding their content are lacking. Image understanding is a key limiting factor in advancing these endeavors. Major challenges remain in understanding the capabilities of the human visual system with respect to biomedical imaging and in extracting and utilizing tacit knowledge of domain experts. To meet these challenges, we propose an innovative, multidisciplinary approach which combines methods of user centered design, visual perception and computer imaging research to interact with domain experts and to elicit and use their extrinsic and intrinsic knowledge. We will use a novel contextual design approach to inspection of dermatology images to discover relationships between perceptually- relevant visual content of images and users' conceptual understanding as expressed through natural language. Analysis of users' eye movements and verbal descriptions, together with mapping to domain medical ontologies, will allow us to integrate visual data with a user-specified language model to define perceptual categories and inform image classification. This is a fundamental and challenging data to knowledge problem that has not been solved. This study will provide proof of concept of the value of eliciting tacit knowledge from domain experts through multiple perceptually relevant modes in order to integrate data and knowledge models for better image understanding and may help enact a paradigm shift in how we conceptualize and develop biomedical information systems, in general.