The Latest Development and Prospects of Histopathological Image Analysis Methods Based on Graph Features

doi:10.11871/jfdc.issn.2096-742X.2024.02.010

Abstract

Abstract:

[Objective] This article aims to review the research progress of artificial intelligence in assisting histopathology analysis in the past five years, mainly focusing on the application of graph feature methods, current problems, and future challenges. [Methods] The article reviews the application of graph theory in histopathological image analysis, including image segmentation, detection, and classification, explores various graph construction algorithms for feature extraction of image topological structures, such as the classic minimum spanning tree algorithm and its derivative innovative algorithms, and analyzes the performance of network structures such as graph convolutional neural networks. [Results] The graph features extracted through structural maps can effectively represent topological information in histopathological images, which helps to achieve accurate tumor segmentation, detection, classification, and cancer grading tasks. In addition, the graph feature method provides a systematic analysis approach by considering global and local features, promoting the understanding of complex tissue pathology images. [Conclusions] The combination of graph features and advanced machine learning technologies has shown strong potential in histopathological image analysis. In the future, these methods will be optimized to improve the accuracy and efficiency of clinical diagnosis.

Key words: histopathological image, graph feature, artificial intelligence, machine learning, tumor assisted diagnosis

HE Ruilin, YANG Xinyi, SUN Hongzan, LI Chen. The Latest Development and Prospects of Histopathological Image Analysis Methods Based on Graph Features[J]. Frontiers of Data and Computing, 2024, 6(2): 101-116, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.02.010.

Figures/Tables 8

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Table 1

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Table 3

Summary of the application of graph method in image classification"

任务	年份	文献	方法	标签	结果评估
二分类	2021	[37]	结合CNN特征提取和GCN的图像关联学习	有监督	分类准确度：98.37%
	2022	[38]	复合扩张的主干网络(composite dilated backbone network (CDBN))	有监督	分类准确度：94.00%
	2023	[39]	CRCCN-Net分类网络模型	有监督	分类准确度：96.26%
	2023	[40]	自适应、可扩展的图卷积网络GraphLSurv	有监督	concordance-index:0.683,比其他方法提高了3.4%
	2023	[42]	GARL-Net网络，深度神经网络DNN	有监督	分类准确度：99.00%
	2021	[43]	支持向量机 (SVM) 和 K-Nearest Neighbour (KNN) 算法	有监督	分类准确度：98.00%
	2022	[44]	GNN+二次谐波生成显微镜图像的胶原纤维形态学特征	有监督	分类准确度：96.20%
	2023	[45]	最小化细胞图（Minimized Cellular Graph，MCG）	有监督	分类准确度：97.70%
	2021	[47]	手工特征（骨骼特征和晶格特征）绘制	有监督
	2022	[48]	深度学习模型特征和手工特征组合提取最佳的混合特征	有监督	分类准确度：91.00%
	2022	[54]	注意全局上下文图卷积神经网络（AGGCN）	有监督	分类准确度：78.20%
	2022	[55]	深度特征图注意网络（DeepGAT）	有监督	分类准确度：95.10%
	2021	[56]	改进对抗生成网络，NAS-SGAN网络	半监督	分类准确度：98.40%
	2023	[58]	异构图的框架和异构图边属性转换器HEAT	伪标签	分类准确度：99.10%
	2021	[59]	基于空间解析的图卷积网络Patch-GCN	弱监督	分类准确度：93.58%
	2021	[30]	图神经网络（GNN）+ 使用对比损失的自监督训练方法	无监督	分类准确度：86.00%
四分类	2023	[41]	分形GCN网络融合到CNN框架中形成CNN-FGCN模型	有监督	分类准确度：95.77%
五分类	2021	[68]	基于多头注意力的多尺度图网络	有监督	分类准确度：71.00%
四分类	2021	[69]	高效网的迁移学习方法	有监督	分类准确度：98.33%
七分类	2022	[70]	图卷积神经网络	有监督	分类准确度：56.25%
四分类	2022	[72]	集成网络模型CNN-GCN	有监督	分类准确度：94.44%
八分类	2022	[74]	依赖性的轻量级卷积神经网络（DBLCNN）	有监督	分类准确度：99.54%
七分类	2022	[76]	新的层次实体图表示（HACT）和层次学习（HACT-Net）方法	有监督	分类准确度：83.79%
三分类	2022	[77]	层次变换图神经网络（HAT-Net+）	无监督	分类准确度：98.00%
三分类	2022	[79]	最小生成树MST	无监督	区分低中高密度区，并提取图特征
三分类	2023	[80]	相关图注意网络（MLP-GAT）	有监督	分类准确度：79.02%

Table 3

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