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Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments

Publication ,  Conference
Mazurowski, MA; Zhang, J; Lo, JY; Kuzmiak, CM; Ghate, SV; Yoon, S
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2014

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. © 2014 SPIE.

Duke Scholars

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

Publication Date

January 1, 2014

Volume

9037
 

Citation

APA
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Mazurowski, M. A., Zhang, J., Lo, J. Y., Kuzmiak, C. M., Ghate, S. V., & Yoon, S. (2014). Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 9037). https://doi.org/10.1117/12.2044404
Mazurowski, M. A., J. Zhang, J. Y. Lo, C. M. Kuzmiak, S. V. Ghate, and S. Yoon. “Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 9037, 2014. https://doi.org/10.1117/12.2044404.
Mazurowski MA, Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon S. Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.
Mazurowski, M. A., et al. “Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9037, 2014. Scopus, doi:10.1117/12.2044404.
Mazurowski MA, Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon S. Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

Publication Date

January 1, 2014

Volume

9037