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- W3089090082 endingPage "101813" @default.
- W3089090082 startingPage "101813" @default.
- W3089090082 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." @default.
- W3089090082 created "2020-10-01" @default.
- W3089090082 creator A5029260213 @default.
- W3089090082 creator A5058290548 @default.
- W3089090082 creator A5070757015 @default.
- W3089090082 date "2021-01-01" @default.
- W3089090082 modified "2023-10-12" @default.
- W3089090082 title "Deep neural network models for computational histopathology: A survey" @default.
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