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English(EN) From Density to Biopsy Decisions and Malignancy Prediction: A Benchmark Study of Multimodal Large Language Models Against Radiologists in Digital and Contrast-Enhanced Mammography

多模态大语言模型在乳腺X线摄影恶性肿瘤预测方面接近放射科医生水平

作者 PulseAugur 编辑部 · [1 个来源] ·

一项最新研究将四种多模态大语言模型（MLLMs）与放射科医生在解读乳腺X线摄影图像以评估乳腺密度、BI-RADS分类、活检候选资格和恶性肿瘤预测方面进行了基准测试。虽然放射科医生在分类任务上普遍优于MLLMs，但特定的掩码MLLMs，特别是Muse Spark和Claude Sonnet 4.6，在估计连续恶性肿瘤概率方面接近人类表现。研究结果表明，MLLMs在乳腺X线摄影分析中可能具有辅助作用，尤其是在提供病灶掩码的情况下。 AI

影响 选定的MLLMs显示出作为放射科医生在乳腺X线摄影中的辅助工具的潜力，特别是在恶性肿瘤概率估计方面。

排序理由 学术论文，展示了AI模型与人类专家进行的基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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多模态大语言模型在乳腺X线摄影恶性肿瘤预测方面接近放射科医生水平

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学术论文，展示了AI模型与人类专家进行的基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan, Alisa Mohebbi, Masume Behruzi, Hamid Khorshidi, Ashkan Ghorbani, Elham Asadiara, Zeinab Khorshidi Lotfi, Ansar Rahman, Nedim Christoph Beste, U. Rajendra Acharya, Sepideh Hatamik… ·

    从密度到活检决策和恶性肿瘤预测：一项多模态大语言模型在数字和增强型乳腺X线摄影中与放射科医生进行基准对比的研究

    arXiv:2609.14676v1 Announce Type: new Abstract: Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessment, biopsy candidacy determination, and continuous malignancy probability estimat…

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