@inproceedings{3d81bb77cbd84543aa32587eac60f055,
title = "Deep BI-RADS Network for Improved Cancer Detection from Mammograms",
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.",
keywords = "Attention, BI-RADS, Breast Cancer, Cancer Detection, Deep Learning, Mammograms, Multi-Modal, Transformer",
author = "Gil Ben-Artzi and Feras Daragma and Shahar Mahpod",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 27th International Conference on Pattern Recognition, ICPR 2024 ; Conference date: 01-12-2024 Through 05-12-2024",
year = "2025",
doi = "10.1007/978-3-031-78104-9_2",
language = "אנגלית",
isbn = "9783031781032",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "17--30",
editor = "Apostolos Antonacopoulos and Subhasis Chaudhuri and Rama Chellappa and Cheng-Lin Liu and Saumik Bhattacharya and Umapada Pal",
booktitle = "Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings",
address = "גרמניה",
}