Body part and imaging modality classification for a general radiology cognitive assistant

Chinyere Agunwa; Mehdi Moradi; Ken C. L. Wong; Tanveer Syeda-Mahmood

doi:10.1117/12.2513074

15 March 2019 Body part and imaging modality classification for a general radiology cognitive assistant

Chinyere Agunwa, Mehdi Moradi, Ken C. L. Wong, Tanveer Syeda-Mahmood

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094910 (2019) https://doi.org/10.1117/12.2513074
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Chinyere Agunwa, Mehdi Moradi, Ken C. L. Wong, and Tanveer Syeda-Mahmood "Body part and imaging modality classification for a general radiology cognitive assistant", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094910 (15 March 2019); https://doi.org/10.1117/12.2513074

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available