Deep BI-RADS Network for Improved Cancer Detection from Mammograms
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929594744963072 |
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| author | Ben-Artzi, Gil Daragma, Feras Mahpod, Shahar |
| author_facet | Ben-Artzi, Gil Daragma, Feras Mahpod, Shahar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep BI-RADS Network for Improved Cancer Detection from Mammograms Ben-Artzi, Gil Daragma, Feras Mahpod, Shahar Computer Vision and Pattern Recognition 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. |
| title | Deep BI-RADS Network for Improved Cancer Detection from Mammograms |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.10894 |