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深度学习用于分级子宫内膜癌.

Manu Goyal1, Laura J Tafe2, James X Feng3

  • 1Department of Biomedical Data Science, Dartmouth College, Hanover, New Hampshire.

The American journal of pathology
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概括
此摘要是机器生成的。

一个人工智能工具EndoNet准确地将子宫内膜癌幻灯片分为高级和低级瘤. 这项技术有助于病理学家对妇科病理瘤进行分级,从而有可能改善患者管理.

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科学领域:

  • 计算病理学计算病理学
  • 人工智能在瘤学中的应用
  • 数字病理学数字病理学

背景情况:

  • 子宫内膜癌是女性普遍存在的恶性瘤,需要精确的组织学和分子分类来进行最佳治疗.
  • 对子宫内膜癌的准确分类对于患者的管理和治疗选择至关重要.

研究的目的:

  • 介绍EndoNet,一个新的深度学习模型来分类子宫内膜癌全幻灯片图像.
  • 评估EndoNet在区分高度和低度子宫内膜癌病例方面的表现.

主要方法:

  • EndoNet使用卷积神经网络进行特征提取,并使用视觉变压器进行幻灯片分类.
  • 该模型被训练在929个数字化血素和素染色的子宫内膜癌整片图像上.
  • 在内部 (110名患者) 和外部 (100名患者) 测试组中评估了性能.

主要成果:

  • 在内部测试组中,EndoNet获得了0.91的加权平均F1分数和0.95的AUC.
  • 在外部测试组中,该模型显示F1得分为0.86和AUC为0.86.
  • 人工智能模型有效地将幻灯片分为低等级 (等级1-2) 和高等级 (等级3,血清性癌症,癌) 的类别.

结论:

  • 恩多网显示出作为人工智能驱动工具的巨大潜力,以协助病理学家对子宫内膜癌等级进行分类.
  • 该模型能够在没有人工注释的情况下对瘤进行分类,这可以简化诊断工作流程.
  • 需要进一步验证,但EndoNet可能支持对妇科病理瘤的分级.