用于组织病理图像分类的双层多实例学习模型  

Double-tier multiple instance learning model for histopathology image classification

作　　者：陆浩 陈金令 陈杰 陈百合 唐卓葳[2] Lu Hao;Chen Jinling;Chen Jie;Chen Baihe;Tang Zhuowei(School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu 610500,China;Mianyang Central Hospital,Mianyang 621000,China)

机构地区：[1]西南石油大学电气信息学院,成都610500 [2]绵阳市中心医院,绵阳621000

出　　处：《中国图象图形学报》2024年第3期811-822,共12页Journal of Image and Graphics

基　　金：四川省重点研发计划项目(2022YFS0020);南充市2022年市校科技战略合作专项项目(22SXQT0292)。

摘　　要：目的 分析组织病理学全玻片图像(whole slide images,WSIs)是病理学诊断的金标准。WSIs具有千兆像素,且通常缺乏像素级标注。弱监督多实例学习是分析WSIs的主流方法,其关键是怎样从大量实例中精确识别出触发类别预测的关键实例。以前的WSIs分析方法主要是在独立同分布假设下设计的,忽略了实例间的相关性和肿瘤的异质性。针对上述问题,提出一种新的双层多实例学习模型。方法 具体地,提出的模型由自适应特征挖掘器和双路交叉检测模块级联构成。首先,第1层的自适应特征挖掘器检索包中的区分性特征,为后续的实例特征聚合生成可靠的内部查询;然后,第2层的双路交叉检测模块通过建模内部查询与实例间的相关性,聚合包中所有实例生成最终的包级表示。此外,在特征提取部分中引入了自监督对比学习方法SimCLR以生成高质量的实例特征。结果在两个公共可用的数据集CAMELYON-16和TCGA(the cancer genome atlas)肺癌上评估了提出的模型,对比分析6种经典的多实例学习模型,结果显示本文模型的性能最优。在准确率方面,所提方法在CAMELYON-16和TCGA肺癌两个数据集上分别达到了95.35%和91.87%,较对比方法中最优的分别高出2.33%和0.96%。结论 提出的模型可以较好地挖掘组织病理学图像的内部特征信息,显著提升检测精度,表明其在病理学诊断应用中的有效性,并能够准确定位病变区域,在病理辅助诊断场景下有较高的应用价值。Objective Whole slide images(WSIs),which refer to scanning and converting a complete microscope slide to digital WSIs,is an efficient technique for visualizing tissue sections in disease diagnosis,medical education,and patho⁃logical research.Analysis of histopathology WSIs is the gold standard for pathology diagnosis.However,analyzing patho⁃logical WSIs is a tedious and time-consuming task,and the diagnosis result is easily influenced by personal experience.The increasing use of WSIs in histopathology results in digital pathology providing huge improvements in pathologists’work⁃flow and diagnosis decision-making,but it also stimulates the need for computer-aided diagnostic tools of WSIs.At present,a significant number of experts and scholars have begun exploring the application of deep learning in the field of patho⁃logical image analysis.WSIs possess gigapixel resolution and usually lack pixel-level annotations.Existing deep learning techniques are developed for small-sized conventional images.Therefore,applying these techniques directly to WSI analy⁃sis is not feasible.Weakly supervised multiple instance learning(MIL)is a powerful method in analyzing WSIs,and the key component is how to effectively discover the crucial instance that triggers the prediction from massive instances and summarize valuable information from different instances.Previous methods were primarily designed based on the indepen⁃dent and identical distribution(i.i.d.)hypothesis,disregarding the relationships among different instances and the hetero⁃geneity of tumors.To solve these problems,a novel double-tier MIL(DT-MIL)model is proposed.Method The proposed method consists of three aspects:1)pre-processing operation of WSIs,2)convolutional neural network(CNN)-based fea⁃ture encoding,and 3)feature fusion of instance embeddings.First,WSIs are cropped into fixed-sized image patches using a sliding window strategy,filtering out invalid background regions and retaining only the foreground areas containing patho⁃logical tissues

关 键 词：多实例学习(MIL) 组织病理学图像 自监督对比学习 弱监督学习 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]