Abstract：
Pathological examination is the optimal approach for diagnosing cancer, and with the advancement of digital imaging technologies, it has spurred the emergence of computational histopathology. The objective of computational histopathology is to assist in clinical tasks through image processing and analysis techniques. In the early stages, the technique involved analyzing histopathology images by extracting mathematical features, but the performance of these models was unsatisfactory. With the development of artificial intelligence (AI) technologies, traditional machine learning methods were applied in this field. Although the performance of the models improved, there were issues such as poor model generalization and tedious manual feature extraction. Subsequently, the introduction of deep learning techniques effectively addressed these problems. However, models based on traditional convolutional architectures could not adequately capture the contextual information and deep biological features in histopathology images. Due to the special structure of graphs, they are highly suitable for feature extraction in tissue histopathology images and have achieved promising performance in numerous studies. In this article, we review existing graph-based methods in computational histopathology and propose a novel and more comprehensive graph construction approach. Additionally, we categorize the methods and techniques in computational histopathology according to different learning paradigms. We summarize the common clinical applications of graph-based methods in computational histopathology. Furthermore, we discuss the core concepts in this field and highlight the current challenges and future research directions.
摘要：
病理检查是诊断癌症的最佳方法，随着数字成像技术的进步，它刺激了计算组织病理学的出现。计算组织病理学的目的是通过图像处理和分析技术来协助临床任务。在早期阶段,该技术涉及通过提取数学特征来分析组织病理学图像，但是这些模型的性能并不令人满意。随着人工智能(AI)技术的发展,传统的机器学习方法应用于该领域。尽管模型的性能有所改善，存在诸如模型泛化差和繁琐的手动特征提取之类的问题。随后，深度学习技术的引入有效地解决了这些问题。然而,基于传统卷积架构的模型无法充分捕获组织病理学图像中的上下文信息和深层生物学特征。由于图的特殊结构，它们非常适合组织病理学图像中的特征提取，并且在许多研究中取得了有希望的性能。在这篇文章中,我们回顾了计算组织病理学中现有的基于图的方法，并提出了一种新颖且更全面的图构造方法。此外,我们根据不同的学习范式对计算组织病理学中的方法和技术进行分类。我们总结了基于图形的方法在计算组织病理学中的常见临床应用。此外，我们讨论了该领域的核心概念，并强调了当前的挑战和未来的研究方向。
