Interpretable deep learning regression for breast density estimation on MRI

Bas H. M. van der Velden; Max A. A. Ragusi; Markus H. A. Janse; Claudette E. Loo; Kenneth G. A. Gilhuijs

doi:10.1117/12.2549003

16 March 2020 Interpretable deep learning regression for breast density estimation on MRI

Bas H. M. van der Velden, Max A. A. Ragusi, Markus H. A. Janse, Claudette E. Loo, Kenneth G. A. Gilhuijs

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131412 (2020) https://doi.org/10.1117/12.2549003
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorithms. These algorithms often rely on segmentation of breast and FGT volume. In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments. We assessed breast density in 506 patients with breast cancer using a regression convolutional neural network (CNN). The input for the CNN were slices of breast MRI of 128 × 128 voxels, and the output was a continuous density value between 0 (fatty breast) and 1 (dense breast). We used 350 patients to train the CNN, 75 for validation, and 81 for independent testing. We investigated why the CNN came to its predicted density using Deep SHapley Additive exPlanations (SHAP). The density predicted by the CNN on the testing set was significantly correlated with the ground truth densities (N = 81 patients, Spearman's ρ = 0:86, P < 0:001). When inspecting what the CNN based its predictions on, we found that voxels in FGT commonly had positive SHAP-values, voxels in fatty tissue commonly had negative SHAP-values, and voxels in non-breast tissue commonly had SHAP-values near zero. This means that the prediction of density is based on the structures we expect it to be based on, namely FGT and fatty tissue. To conclude, we presented an interpretable deep learning regression method for breast density estimation on MRI with promising results.

Conference Presentation

Citation Download Citation

Bas H. M. van der Velden, Max A. A. Ragusi, Markus H. A. Janse, Claudette E. Loo, and Kenneth G. A. Gilhuijs "Interpretable deep learning regression for breast density estimation on MRI", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131412 (16 March 2020); https://doi.org/10.1117/12.2549003

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