Survey of Breast Cancer Pathological Image Analysis Methods Based on Graph Neural Networks
Pathological diagnosis is the gold standard for cancer diagnosis and treatment,the use of artificial intelligence(AI)models for analyzing pathological images has the potential to not only reduce the workload of pathologists but also improve the accuracy of cancer diagnosis and treatment.However,these methods face challenges due to the large scale of pathological images and the difficulty in interpreting the predicted results.In recent studies,graph neural networks have shown their strong abilities in modeling spatial context and interpretability of entities in images,which provides a new idea for the study of digital pathology.In this survey,we review recent related works in computer vision,analyze the advantages of graph neural networks for breast cancer pathology,classify and compare existing graph construction methods,and analyze and compare graph neural network models pro-posed in recent years.We also summarize the challenges that exist in using graph neural networks for analyzing pathological ima-ges of breast cancer and prospect the future research directions.
Breast cancer pathological imageGraph neural networkGraph classificationDigital pathology
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