Quality controlled segmentation to aid disease detection

Mehdi Moradi; Ken C. L. Wong; Alexandros Karargyris; Tanveer Syeda-Mahmood

doi:10.1117/12.2549426

16 March 2020 Quality controlled segmentation to aid disease detection

Mehdi Moradi, Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141K (2020) https://doi.org/10.1117/12.2549426
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Basic deep learning classifiers used for medical images often produce global labels. While annotation for localized disease detection might be costly, the knowledge of prevalence of conditions in different anatomical areas can help improve the accuracy by limiting the classifier to relevant areas. However, this improvement provided by context knowledge, is usually offset by the errors of the segmentation map used to isolate the area of interest. This paper proposes a framework for disease classification consisting of a segmentation network, a segmentation quality assessment network, and two separate classifiers on whole image and relevant segmented area. The quality assessment network controls the impact of the two disease classifiers on the final outcome, utilizing the masked image only when segmentation is acceptable. We show that in a very large dataset of chest X-ray images, this framework produces a 2% increase in the area under ROC curve for classification compared to a baseline.

Conference Presentation

Citation Download Citation

Mehdi Moradi, Ken C. L. Wong, Alexandros Karargyris, and Tanveer Syeda-Mahmood "Quality controlled segmentation to aid disease detection", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141K (16 March 2020); https://doi.org/10.1117/12.2549426

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