Histopathological images contain rich phenotypic information that can be used
to monitor underlying mechanisms contributing to diseases progression and
patient survival outcomes. Recently, deep learning has become the mainstream
methodological choice for analyzing and interpreting cancer histology images.
In this paper, we present a comprehensive review of state-of-the-art deep
learning approaches that have been used in the context of histopathological
image analysis. From the survey of over 130 papers, we review the fields
progress based on the methodological aspect of different machine learning
strategies such as supervised, weakly supervised, unsupervised, transfer
learning and various other sub-variants of these methods. We also provide an
overview of deep learning based survival models that are applicable for
disease-specific prognosis tasks. Finally, we summarize several existing open
datasets and highlight critical challenges and limitations with current deep
learning approaches, along with possible avenues for future research.

本文对如何使用深度学习应对肿瘤组织病理图片的问题进行了综述和分析，从 130 多篇研究论文中选出机器学习策略、生存模型、现有数据集和研究中存在的挑战和限制等方面进行总结和梳理。