Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.

The histopathological image is widely considered as the gold standard for the diagnosis and prognosis of human cancers. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology analysis. The aim of our paper is to provide a comprehensive and up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis, including color normalization, nuclei/tissue segmentation, and cancer diagnosis and prognosis. The experimental results of the existing studies demonstrated that deep learning is a promising tool to assist clinicians in the clinical management of human cancers. With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

组织病理学图像被广泛认为是人类癌症诊断和预后的金标准。最近，深度学习技术在计算机视觉领域取得了极大的成功，这也激发了人们对数字病理分析的浓厚兴趣。我们论文的目的是对用于数字H&E染色病理图像分析的深度学习方法进行全面且最新的综述，包括颜色归一化、细胞核/组织分割以及癌症诊断和预后。现有研究的实验结果表明，深度学习是协助临床医生进行人类癌症临床管理的一种有前途的工具。 随着数字组织病理学取得显著成功，我们目睹了用于分析数字病理和活检图像块的计算方法的迅速扩展。然而，组织病理学图像前所未有的规模和异质性模式带来了关键的计算瓶颈，需要新的计算组织病理学工具。最近，深度学习技术在计算机视觉领域取得了极大的成功，这也激发了人们对数字病理应用的浓厚兴趣。深度学习及其扩展为解决许多具有挑战性的组织病理学图像分析问题开辟了若干途径，包括颜色归一化、图像分割以及人类癌症的诊断/预后。在本文中，我们对用于数字H&E染色病理图像分析的深度学习方法进行了全面且最新的综述。具体而言，我们首先描述了近期使用深度学习进行颜色归一化的文献，这是H&E染色组织病理学图像分析的一个重要研究方向。在讨论颜色归一化之后，我们回顾了深度学习方法在各种H&E染色图像分析任务（如细胞核和组织分割）中的应用。我们还总结了几项使用深度学习从H&E染色组织病理学图像中进行人类癌症诊断和预后的关键临床研究。最后，为了方便对这个令人兴奋的领域感兴趣的研究人员，本文还提供了病理图像分析的在线资源和开放性研究问题。