Deep neural network models for computational histopathology: A survey

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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a comprehensive review of state-of-the-art deep learning approaches used in histopathological image analysis. Through a survey of over 130 papers, the progress in the field based on different machine learning strategies is reviewed. Additionally, the paper discusses the application of deep learning in survival models and highlights the challenges and limitations of current deep learning methods, as well as potential directions for future research.