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11 March 2014 Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments
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Abstract
Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.
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Maciej A. Mazurowski, Jing Zhang, Joseph Y. Lo, Cherie M. Kuzmiak, Sujata V. Ghate M.D., and Sora Yoon "Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370S (11 March 2014); https://doi.org/10.1117/12.2044404
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