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English(EN) Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images

深度学习模型从组织形态学预测结直肠癌生存率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为MorphContrast的新深度学习框架，利用全切片图像预测结直肠癌的生存率。这种形态感知对比学习方法使用组织标签作为结构监督，结合门控注意力池化和辅助目标来提高预后辨别能力。该框架在回顾性分析和外部验证中显示出有希望的结果，表明组织形态学可以作为癌症预后中结果感知表征学习的有效结构监督。 AI

影响 这项研究可能有助于改善结直肠癌患者的风险分层和个性化治疗方案。

排序理由 该集群包含一篇详细介绍用于医学图像分析的新深度学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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深度学习模型从组织形态学预测结直肠癌生存率

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该集群包含一篇详细介绍用于医学图像分析的新深度学习框架的研究论文。[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) · Hikmat Khan, Metin N. Gurcan, Anil Parwani, Wendy L. Frankel, Wei Chen, Muhammad Khalid Khan Niazi ·

    形态感知深度学习从全切片图像预测结直肠癌生存率

    arXiv:2610.17603v1 Announce Type: cross Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and the tissue morphologies carrying prognostic information remain incom…

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