Poster + Paper
3 April 2024 Understanding impact of textural changes for mammogram analysis using counterfactuals
Ridhi Arora, Juhun Lee
Author Affiliations +
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.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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KEYWORDS
Image classification

Mammography

Control systems

Education and training

Image segmentation

Computer aided detection

Simulations

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