Abstract
Simple SummaryThe 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.
Highlights
Deep Neural NetworkDeep learning is a new research direction in the field of machine learning based on the deep neural network, which has greatly boosted the performance of natural image analysis techniques, such as image classification [24], object detection [25], and semantic segmentation [26]
A wide variety of biomarkers been the utilized for the diagnosis can cause variance across different samples, therebyhave causing difficulties of handand prognosis cancers, including radiomics images[23]
Deep learning is a new research direction in the field of machine learning based on the deep neural network, which has greatly boosted the performance of natural image analysis techniques, such as image classification [24], object detection [25], and semantic segmentation [26]
Summary
Deep learning is a new research direction in the field of machine learning based on the deep neural network, which has greatly boosted the performance of natural image analysis techniques, such as image classification [24], object detection [25], and semantic segmentation [26]. ×ing protocols ×across different ×pathology labs, interpatient variabilities, and slide scanner variations Such color variance will affect the generalization performance of deep learning models. Protocols across different pathology labs, interpatient variabilities, and slide scanner variations StainGAN, which could achieve better qualitative performance in normalizing different generative model, which was trained in an end-to-end manner and could be instantly apimages (Figure 4). Inspired by the cycle-GAN [47], which could be successfully apof the histopathological images and integrated semantic information at different layers plied to image-style transformation, Shaban et stain al. Other works [38,39] considered the structural integrity of the histopathological images and integrated semantic information at different layers between a pre-trained semantic network and the stain color normalization network to further improve the normalization performance
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