ICADx: interpretable computer aided diagnosis of breast masses

Seong Tae Kim; Hakmin Lee; Hak Gu Kim; Yong Man Ro

doi:10.1117/12.2293570

27 February 2018 ICADx: interpretable computer aided diagnosis of breast masses

Seong Tae Kim, Hakmin Lee, Hak Gu Kim, Yong Man Ro

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057522 (2018) https://doi.org/10.1117/12.2293570
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Conference Presentation

Citation Download Citation

Seong Tae Kim, Hakmin Lee, Hak Gu Kim, and Yong Man Ro "ICADx: interpretable computer aided diagnosis of breast masses", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057522 (27 February 2018); https://doi.org/10.1117/12.2293570

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