English(EN) Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies

AI模型预测良性乳腺肿瘤以减少不必要活检

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个多模态人工智能系统，该系统结合了图像和文本分析来预测乳腺肿瘤是否为良性，旨在减少不必要活检。该系统使用 Vision Transformer 处理图像特征，使用 DeBERTa-V3-Large 处理文本，并利用 Grad-CAM 提供其预测的可视化解释。在对 1,632 名患者的测试中，多模态方法实现了 0.72 的 AUC，优于仅图像或仅文本的模型，并显示出降低医疗成本和减轻患者焦虑的潜力。 AI

影响通过最大限度地减少不必要医疗程序，可以提高诊断准确性并降低医疗成本。

排序理由详细介绍一种用于医学诊断的新人工智能方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sadam Hussain, Mansoor Ali, Daly Betzabeth Avenda\~no-Avalos, Jose Gerardo Tamez-Pe\~na · 2026-10-27 04:00

用于减少不必要乳腺活检的可解释多模态人工智能方法

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…

本文如何被排名

报道来源 [1]

用于减少不必要乳腺活检的可解释多模态人工智能方法

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