Understanding impact of textural changes for mammogram analysis using counterfactuals

Ridhi Arora; Juhun Lee

doi:10.1117/12.3008526

3 April 2024 Understanding impact of textural changes for mammogram analysis using counterfactuals

Ridhi Arora, Juhun Lee

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292721 (2024) https://doi.org/10.1117/12.3008526
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this study, we explored the significance of texture information for lesion detection using counterfactuals, a framework that can explain causal situation by exploring different actions or conditions and its associated results. Specifically, we investigated counterfactuals on mammogram texture; one example could be “what would happen on classifiers if there is no texture (or grayed-out) inside/outside the lesion.” We used the dataset of 10,415 2D screening mammograms, including 4,942 recalled lesions (BI-RADS 0) with lesion masks and 5,473 normal cases (BI-RADS 1). For identifying foreground and background textures, we applied lesion masks directly on lesion cases, while artificially imposed selected lesion masks on normal controls. Using this, four counterfactual cases were examined: 1) replacing lesion foreground (LF) with its mean intensity (MI) vs. normal (N), 2) replacing normal foreground (NF) with its MI vs. lesion (L), 3) replacing lesion background (LB) with its MI vs. N, and 4) replacing normal background (NB) with its MI vs. L. We trained three convolutional neural networks, which were ResNet50v2, ResNet50, and MobileNet, to classify lesion and normal cases (non-counterfactual, baseline). The classifiers on the first three counterfactual cases (LF vs. N, L vs. NF, and LB vs. N) performed similar to the baseline, while their performances on the cases of NB vs. L was significant dropped (p-value < 0.0001). Our findings indicate that lesion shape plays a key role for lesion image classification, while background texture is important for classifying normal cases.

Citation Download Citation

Ridhi Arora and Juhun Lee "Understanding impact of textural changes for mammogram analysis using counterfactuals", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292721 (3 April 2024); https://doi.org/10.1117/12.3008526

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