With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches. However, learning over patch-wise features using convolutional neural networks limits the ability of the model to capture global contextual information and comprehensively model tissue composition. The phenotypical and topological distribution of constituent histological entities play a critical role in tissue diagnosis. As such, graph data representations and deep learning have attracted significant attention for encoding tissue representations, and capturing intra- and inter- entity level interactions. In this review, we provide a conceptual grounding for graph analytics in digital pathology, including entity-graph construction and graph architectures, and present their current success for tumor localization and classification, tumor invasion and staging, image retrieval, and survival prediction. We provide an overview of these methods in a systematic manner organized by the graph representation of the input image, scale, and organ on which they operate. We also outline the limitations of existing techniques, and suggest potential future research directions in this domain.
翻译:由于预测问题的代表性学习取得了显著的成功,我们目睹了利用机器学习和深层次学习来分析数字病理学和生物心理图像片段的迅速扩大,然而,利用进化神经网络对补进特征的学习限制了模型捕捉全球背景信息和全面模型组织构成的能力。组织组织诊断中,成份生理实体的个性分布和结构学分布具有关键作用。因此,图表数据表述和深层次学习吸引了对编码组织表述的极大关注,并捕捉了实体内部和实体之间的相互作用。在本次审查中,我们为数字病理学的图表分析提供了概念基础,包括实体绘图结构和图形结构,并展示了它们目前在肿瘤本地化和分类、肿瘤入侵和作用、图像检索和生存预测方面所取得的成功。我们系统地概述了这些方法,这些方法是由输入图像、规模和它们所操作的器官的图形表示。我们还概述了现有技术的局限性,并提出了该领域未来可能的研究方向。