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Body part and imaging modality classification for a general radiology cognitive assistant
Author(s): Chinyere Agunwa; Mehdi Moradi; Ken C. L. Wong; Tanveer Syeda-Mahmood
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Paper Abstract

Decision support systems built for radiologists need to cover a fairly wide range of image types, with the ability to route each image to the relevant algorithm. Furthermore, the training of such networks requires building large datasets with significant efforts in image curation. In situations where the DICOM tag of an image is unavailable, or unreliable, a classifier that can automatically detect the body part depicted in the image, as well as the imaging modality, is necessary. Previous work has shown the use of imaging and textual features to distinguish between imaging modalities. In this work, we present a model for the simultaneous classification of body part and imaging modality, which to our knowledge has not been done before, as part of the larger work to create a cognitive assistant for radiologists. This classification network consists of 10 classes built from a VGG network architecture using transfer learning to learn generic features. An accuracy of 94.8% is achieved.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094910 (15 March 2019); doi: 10.1117/12.2513074
Show Author Affiliations
Chinyere Agunwa, IBM Research - Almaden (United States)
Mehdi Moradi, IBM Research - Almaden (United States)
Ken C. L. Wong, IBM Research - Almaden (United States)
Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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