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Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies — arXiv:2610.35704

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.35704
License (as archived): CC0-1.0
Archived: 2026-10-27T07:59:12.160Z
SHA-256 of the archived text: f53d2324a2db17e31e5bbe521e79cc9bc10102ebe2bcb0ff35efc75a8caa81ba
Stable URL of this page: https://metraipolis.com/documento/dc_cd3e538e49bb

How to cite

Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies — arXiv:2610.35704 — arXiv (en), https://arxiv.org/abs/2610.35704 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd3e538e49bb (Metraipolis archive dc_cd3e538e49bb, sha256 f53d2324a2db17e31e5bbe521e79cc9bc10102ebe2bcb0ff35efc75a8caa81ba)

BibTeX

@misc{dccd3e538e49bb,
  title = {Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies — arXiv:2610.35704},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.35704},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd3e538e49bb; sha256 f53d2324a2db17e31e5bbe521e79cc9bc10102ebe2bcb0ff35efc75a8caa81ba},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.35704. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, particularly for predicting biopsy outcomes remains limited. This study presents a multimodal vision-language approach, incorporating Grad-CAM as an Explainable AI (XAI) technique, to predict benign tumors in patients recommended for biopsy, especially those classified as BI-RADS 4 and 5. A novel multimodal framework was devel- oped using Vision Transformer (ViT) for imaging feature extraction and DeBERTa-V3-Large for textual embeddings. The model was trained and evaluated on a comprehensive in-house dataset comprising 1,632 patients, each with four mammography views and a corresponding radiology report. The features of both modalities were extracted independently and fused by late fusion before classification. Grad-CAM was used to generate visual explanations highlighting key regions influencing model predictions. The proposed model demonstrated high sensitivity 90%, 95%, 99%, and 100% with corresponding specificities of 40%, 36%, 18%, and 11%, respectively. The best AUC achieved was 0.72 by multimodal approach on the test set, outperforming the image only (0.66) and text-only (0.68) baselines. The proposed multimodal architecture, enhanced with Grad-CAM, demonstrates a strong potential to identify benign tumors among high-risk patients, offering interpretable insights and thereby helping reduce unnecessary biopsies, patient anxiety, and healthcare costs.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.35704 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T07:55:30.767Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).