基于图特征的组织病理学图像分析方法的最新发展情况与展望OA

[Objective]This article aims to review the research progress of artificial intelligence in assist-ing 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 ap-plication of graph theory in histopathological image analysis,including image segmentation,de-tection,and classification,explores various graph construction algorithms for feature extraction of image topological structures,such as the classic minimum spanning tree algorithm and its de-rivative 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 accu-rate 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 learn-ing 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.