Identifying disease-free chest x-ray images with deep transfer learning

Ken C. L. Wong; Mehdi Moradi; Joy Wu; Tanveer Syeda-Mahmood

doi:10.1117/12.2513164

13 March 2019 Identifying disease-free chest x-ray images with deep transfer learning

Ken C. L. Wong, Mehdi Moradi, Joy Wu, Tanveer Syeda-Mahmood

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500P (2019) https://doi.org/10.1117/12.2513164
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Chest X-rays (CXRs) are among the most commonly used medical image modalities. They are mostly used for screening, and an indication of disease typically results in subsequent tests. As this is mostly a screening test used to rule out chest abnormalities, the requesting clinicians are often interested in whether a CXR is normal or not. A machine learning algorithm that can accurately screen out even a small proportion of the “real normal” exams out of all requested CXRs would be highly beneficial in reducing the workload for radiologists. In this work, we report a deep neural network trained for classifying CXRs with the goal of identifying a large number of normal (disease-free) images without risking the discharge of sick patients. We use an ImageNet-pretrained Inception-ResNet-v2 model to provide the image features, which are further used to train a model on CXRs labelled by expert radiologists. The probability threshold for classification is optimized for 100% precision for the normal class, ensuring no sick patients are released. At this threshold we report an average recall of 50%. This means that the proposed solution has the potential to cut in half the number of disease-free CXRs examined by radiologists, without risking the discharge of sick patients.

Conference Presentation

Citation Download Citation

Ken C. L. Wong, Mehdi Moradi, Joy Wu, and Tanveer Syeda-Mahmood "Identifying disease-free chest x-ray images with deep transfer learning", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500P (13 March 2019); https://doi.org/10.1117/12.2513164

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