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AI model uses explainable AI to reduce unnecessary breast biopsies

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel multimodal AI approach using Grad-CAM for explainability to predict benign breast tumors, aiming to reduce unnecessary biopsies. The system integrates a Vision Transformer for image analysis and DeBERTa-V3-Large for radiology report interpretation. Tested on 1,632 patients, the model achieved an AUC of 0.72, outperforming image-only and text-only baselines, and offers interpretable visual explanations. AI

IMPACT Potential to reduce unnecessary medical procedures and associated costs through improved diagnostic accuracy.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new AI model and its performance on a specific task.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI model uses explainable AI to reduce unnecessary breast biopsies

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15 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sadam Hussain, Mansoor Ali, Daly Betzabeth Avenda\~no-Avalos, Jose Gerardo Tamez-Pe\~na ·

    Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies

    arXiv:2610.35704v1 Announce Type: new Abstract: 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 mul…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies

    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 …

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