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English(EN) Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations

计算病理学中的可解释人工智能:提出新框架

一篇新发表在arXiv上的综述论文,探讨了计算病理学领域可解释人工智能(XAI)研究的碎片化问题。该论文提出了标准化的词汇、XAI方法分类以及一个将临床问题映射到推荐XAI方法的框架。它指出了阻碍临床应用的关键差距,并提出了在该医学领域推进XAI的可行步骤。 AI

影响 标准化XAI术语和方法,可能加速AI在临床病理学中的安全应用。

排序理由 该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

计算病理学中的可解释人工智能:提出新框架

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该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shubham Innani, Suhang You, Adam Shephard, Bhakti Baheti, Francesco Ciompi, Joe Yeong, Nasir Rajpoot, Michael Feldman, Solene Florence Kammerer-Jacquet, Dimitrios Makris, Geert Litjens, Anne L. Martel, Jana Lipkova, April Khademi, Spyridon Bakas, for the… ·

    计算病理学中的可解释人工智能 (XAI):定义、分类和建议

    arXiv:2608.28820v1 Announce Type: new Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gigapixel whole-slide images. Clinical adoption is prog…