数字病理学自我监督培训模型的普遍性:结肠直肠癌的多国比较
Zhuchen Shao1, Liuxi Dai2, Jitendra Jonnagaddala3
1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
JCO clinical cancer informatics
|September 13, 2023
概括
在未标记的结肠直肠癌 (CRC) 病理图像上进行自我监督学习 (SSL) 优于传统方法. 与单一国家或ImageNet预训练相比,多国SSL数据可以提高预测任务性能和概括性.
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
- 计算病理学计算病理学
背景情况:
- 数字病理学产生了大量未标记的成像数据.
- 有效利用这些数据对于推进结直肠癌 (CRC) 研究和诊断至关重要.
- 自主监督学习 (SSL) 提供了一种有希望的方法来利用未标记的数据.
研究的目的:
- 评估SSL在结直肠癌 (CRC) 相关预测任务中的有效性.
- 将SSL预训与ImageNet预训用于CRC预测任务进行比较.
- 评估多国与单一国家预训数据对模型性能的影响.
主要方法:
- 利用SimSiam对来自美国和澳大利亚的大型,未标记的全幻灯片图像CRC数据集进行自我监督的预训.
- 将SSL预训练的编码器应用于各种监督 (组织分类,MSI状态) 和弱监督 (多重体分类,生存预测) 任务.
- 对比在ImageNet上预训练的模型和评估的单国与双国预训练策略的性能.
主要成果:
- 在SSL预训练的表现明显优于ImageNet预训练,在监督 (3.01%的增长) 和弱监督 (1.53%的AUC增长) 任务上均获得更高的平均分数.
- 两国SSL预训显示出比单一国家预训更稳定的表现和更好的概括性.
- 两个国家的预训练显示,与单一国家的预训练相比,在监督和弱监督任务上,AUC的平均改善率分别为1.93%和1.36%.
结论:
- 使用未标记的数字病理图像的自主监督学习 (SSL) 是CRC相关预测任务的更有效策略,而不是ImageNet预训.
- 利用多国数据集进行SSL预训练可以提高CRC分析中的模型稳定性和概括能力.
- 在瘤学中,SSL是一个强大的工具,可以从大型,未标记的病理学数据集中提取有意义的见解.
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