Deep neural network models for computational histopathology: A survey.

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ID: 171472
2020
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Abstract
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.
Reference Key
srinidhi2020deepmedical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Srinidhi, Chetan L;Ciga, Ozan;Martel, Anne L;
Journal Medical image analysis
Year 2020
DOI
S1361-8415(20)30177-8
URL
Keywords
Deep learning digital pathology convolutional neural networks survey review computational histopathology histology image analysis

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