Computer discrimination between diseases of the brain based on MR image features

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A socio-cognitive engineering approach to the development of a knowledge-based training system for neuroradiology

Tutoring systems could satisfy a demand of many professions for structured casebased training, but to be accepted they need to be robust, authoritative and matched to the needs of trainees in the workplace. This paper outlines a methodology for the development of knowledge-based training that integrates software, task, knowledge and organizational engineering. It consists of a set of "building blocks" that specify the type of activities needed to develop a complete knowledge-based training system, while allowing flexibility in the choice and ordering of specific design techniques. The approach is illustrated by a project to develop the MR Tutor, a knowledge-based training system for neuroradiology. The building blocks for this project have included an analysis of published studies of cognitive processes in medical image interpretation, elicitation and refinement of knowledge from an expert neuroradiologist, workplace studies of radiology training and experiments with new techniques for data visualization. The MR Tutor gives trainee radiologists the experience of observing and analysing a large archive of cases and practice in comparing their interpretations with those of experts. It is based on a structured language for describing abnormal appearance in Magnetic Resonance images of the head, and it uses a novel "overview plot" to visualize and interact with the image archive. The development methodology has been followed to the stage of implementing a robust integrated system

Statistical support for uncertainty in radiological diagnosis

Radiological interpretation and diagnosis involves the comparison and classification of complex medical images and is typical of the categorisation tasks that have been the subject of observational studies in Cognitive Science. This paper considers the affinity between statistical modelling and theories of categorisation for naturally occurring categories. Statistical based measures of similarity and typicality with a probabilistic interpretation are derived. The utilisation of these measures in the support of diagnosis under uncertainty via interactive overview plots is described. The application of the methodology to magnetic resonance imaging of the head is considered. The methods detailed have application to other fields involving archiving and retrieving of image data

Computer based neuroradiology training using a "small worlds" approach to differential diagnosis

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Training in image description and diagnosis for MR radiology of the brain

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