在肺腺癌中同时分类和划分七种类型的组织生长模式,使用自主监督学习和在线硬补丁挖掘
Tongning Wu1, Qixuan Wang1, Congsheng Li2
1Artificial Intelligence Research Institute, China Academy of Information and Communications Technology, Beijing, China.
Quantitative imaging in medicine and surgery
|November 10, 2025
概括
这项研究开发了一个深度学习框架,以在全幻灯片图像中对肺腺癌生长模式进行分类,实现病理学家级准确性和效率,以改善癌症分类.
科学领域:
- 数字病理学数字病理学
- 人工智能在瘤学中的应用
- 计算生物学是一种计算生物学.
背景情况:
- 肺腺癌 (LUAD) 的分级依赖于在全幻灯片图像 (WSIs) 中识别生长模式.
- 手动WSI检查是耗时和主观的,影响肺癌分级准确度.
- 开发自动化方法对于高效可靠的LUAD分类至关重要.
研究的目的:
- 开发基于补丁的深度学习 (DL) 框架,以准确地分类和划分LUAD增长模式.
- 解决手动WSI分析在时间和专业知识方面的局限性.
- 为了达到与经验丰富的病理学家可比的DL性能.
主要方法:
- 一个基于贴片的DL框架被设计用于分类正常的肺组织和七个LUAD生长模式.
- 自主监督学习,一致性规范化和伪标签被结合起来,以提高模型性能.
- 实施了在线硬补丁挖掘策略,以改善对具有挑战性的案例的特征提取.
主要成果:
- 该DL框架实现了高精度 (95.3%) 和回忆 (96.8%),其IOU为87.5%.
- 性能优于现有的最先进的模型,与经验丰富的病理学家相比 (科恩的卡帕=0.97).
- 该模型可以在不到1分钟的时间内分析WSI,显示出高效率.
结论:
- 拟议的DL框架准确地分类了LUAD生长模式,与病理学家的表现相匹配.
- 模型生成的预测概率地图有助于病变可视化,用于分类和分阶段.
- 整合DL结果可以提高病理学家的诊断准确性和可靠性.
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