冲浪者:一种可解释的模式感知生存变压器,用于从组织病理学全幻灯片图像中预测癌症生存率
Zhikang Wang1, Qian Gao2, Xiaoping Yi2
1Xiangya Hospital, Central South University, Changsha, Hunan, PR China; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia.
Computer methods and programs in biomedicine
|August 12, 2023
概括
我们开发了Surformer,这是一个可解释的神经网络,用于从整个幻灯片图像 (WSI) 预测癌症存活率. 冲浪者量化组织学模式,优于现有方法,并提供了对生存预测的明确见解.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在瘤学中
背景情况:
- 高分辨率的组织病理学整片图像 (WSIs) 在癌症中具有显著的预后价值.
- 目前用于生存预测的计算病理学方法往往缺乏解释性.
- 在癌症预后中需要可解释的AI模型.
研究的目的:
- 介绍Surformer,一种高度可解释的神经网络,用于使用WSIs预测癌症存活率.
- 为了解决现有的计算病理学方法的解释性限制.
- 为了实现组织学模式的定量分析,以改善生存预测.
主要方法:
- 冲浪者使用袋级标签量化组织学模式,没有补丁/细胞级数据.
- 以比率保留的交叉注意模块 (RRCA) 使用可学习原型生成特征,并通过比率因子 (rfs) 量化补丁相关性.
- 多头自我和交叉注意模块增强了功能,而解损失函数则为明确量化促进了独特的模式焦点.
主要成果:
- 与最先进的方法相比,Surformer 在五个TCGA数据集中表现出优越的性能.
- 该研究成功地可视化了RF分布,以区分高风险和低风险患者队列.
- 确定并分析了影响生存预测的关键组织学模式.
结论:
- 冲浪者提供了一个有价值的,可解释的工具,用于他的病理学图像分析.
- 该模型促进了数据驱动的洞察力,了解组织病理图像和患者存活率之间的关系.
- 冲浪者有助于理解WSIs的癌症预后的生物学基础.
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