Srinidhi Chetan L, Ciga Ozan, Martel Anne L
Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Canada.
Department of Medical Biophysics, University of Toronto, Canada.
Med Image Anal. 2021 Jan;67:101813. doi: 10.1016/j.media.2020.101813. Epub 2020 Sep 25.
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting 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 field's 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.
组织病理学图像包含丰富的表型信息，可用于监测导致疾病进展和患者生存结果的潜在机制。最近，深度学习已成为分析和解释组织学图像的主流方法选择。在本文中，我们对在组织病理学图像分析背景下使用的最新深度学习方法进行了全面综述。通过对130多篇论文的调研，我们基于不同机器学习策略（如监督学习、弱监督学习、无监督学习、迁移学习以及这些方法的各种其他子变体）的方法学方面来回顾该领域的进展。我们还概述了适用于特定疾病预后任务的基于深度学习的生存模型。最后，我们总结了几个现有的开放数据集，强调了当前深度学习方法面临的关键挑战和局限性，以及未来研究可能的途径。
