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

Abstract

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.

Keywords: a whole-slide pathological imaging (WSI); artificial intelligence; color normalization; diagnosis and prognosis; digital pathology image analysis; machine learning; segmentation.

Figure 1

General research directions for digital pathology image analysis.

Figure 2

The general architecture of a convolutional neural network.

Figure 3

Overview of papers using deep learning for color normalization in histopathological images [35,36,37,38,39,40,41].

Figure 4

Stain style transfer for digital histopathological images.

Figure 5

Overview of papers using deep learning for nuclei/tissue segmentation in histopathological images [51,53,54,55,56,57,58,59,60,61,62,63,64].

Figure 6

Triple U-Net: hematoxylin-aware nuclei segmentation with progressive dense feature aggregation.

Figure 7

Overview of papers using deep learning for diagnosis and prognosis of the disease in histopathology images [108,109,110,111,112,113,114,115,116].

Figure 8

Weakly-supervised deep ordinal Cox model (BDOCOX) for survival prediction from WSI.