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English(EN) Learning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification

新AI方法使用患者标签训练癌症登记模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的框架，使用基于注意力机制的多示例学习（ABMIL）来训练癌症登记任务的深度学习模型，而无需单独的报告注释。该方法利用通常未直接链接到个体病理报告的现有患者级别标签来创建高质量的训练数据集。ABMIL方法在BC癌症登记处的肿瘤分组分类方面表现出有效性，宏观F1得分达到0.83，优于其他方法。 AI

影响 这种方法可以通过更好地利用现有数据来显著提高癌症登记工作流程的效率和准确性。

排序理由 该集群包含一篇详细介绍特定领域新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新AI方法使用患者标签训练癌症登记模型

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该集群包含一篇详细介绍特定领域新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Leonard Ruocco, Jonathan Simkin, Lovedeep Gondora, Gregory Arbour, Raymond Ng ·

    从丢失的来源中学习：多实例学习用于癌症登记肿瘤分组分类

    arXiv:2607.03481v1 Announce Type: new Abstract: Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constrained by the scarcity of report-level human-annotate…

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