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arXiv · 2411.10894

Deep BI-RADS Network for Improved Cancer Detection from Mammograms

Gil Ben-Artzi·Feras Daragma·Shahar Mahpod
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Abstract

While state-of-the-art models for breast cancer detection leverage multi-view mammograms for enhanced diagnostic accuracy, they often focus solely on visual mammography data. However, radiologists document valuable lesion descriptors that contain additional information that can enhance mammography-based breast cancer screening. A key question is whether deep learning models can benefit from these expert-derived features. To address this question, we introduce a novel multi-modal approach that combines textual BI-RADS lesion descriptors with visual mammogram content. Our method employs iterative attention layers to effectively fuse these different modalities, significantly improving classification performance over image-only models. Experiments on the CBIS-DDSM dataset demonstrate substantial improvements across all metrics, demonstrating the contribution of handcrafted features to end-to-end.

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Gil Ben-Artzi, Feras Daragma, Shahar Mahpod. 2024-11-16. Deep BI-RADS Network for Improved Cancer Detection from Mammograms. https://arxiv.org/abs/2411.10894

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