A survey on graph-based deep learning for computational histopathology
A survey on graph-based deep learning for computational histopathology
复制标题
DOI：
10.1016/j.compmedimag.2021.102027
复制
发表时间：
2022-01-01
影响因子：
5.7
通讯作者：
Petersson, Lars
中科院分区：
文献类型：
--
作者：
Ahmedt-Aristizabal, David;Armin, Mohammad Ali;Petersson, Lars
随着表征学习在预测问题上的显著成功，我们见证了机器学习和深度学习在数字病理和活检图像补丁分析中的快速扩展。然而，使用卷积神经网络学习补丁智能特征限制了模型捕获全局上下文信息和全面建模组织组成的能力。组成组织实体的表型和拓扑分布在组织诊断中起着关键作用。因此，图数据表示和深度学习在编码组织表示和捕获实体内部和实体之间的交互方面引起了极大的关注。在这篇综述中，我们提供了数字病理学中图形分析的概念基础，包括实体图构建和图形架构，并介绍了它们目前在肿瘤定位和分类、肿瘤侵袭和分期、图像检索和生存预测方面的成功。我们以系统的方式对这些方法进行概述，这些方法由输入图像的图形表示、比例和它们操作的器官组织起来。我们还概述了现有技术的局限性，并提出了该领域潜在的未来研究方向。
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.