Frontiers of Data and Computing ›› 2024, Vol. 6 ›› Issue (2): 101-116.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.02.010

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

• Special Issue: Advance of Intelligent Healthcare • Previous Articles     Next Articles

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

HE Ruilin1(),YANG Xinyi1,SUN Hongzan2,LI Chen1,*()   

  1. 1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110819, China
    2. Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
  • Received:2023-11-08 Online:2024-04-20 Published:2024-04-26


[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