Deep neural network models for computational histopathology: A survey

By Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel. Medical Image Anal., 2019

725 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/e11ae93424c4c53f5cb6ab4de90a9469c2172604

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Citations in the corpus

Harnessing multimodal data integration to advance precision oncology (2021, methodology): “Preprocessing techniques in MRI 122 and H&E staining 123,124 address this heterogeneity, and with large cohorts, DL is somewhat robust with regard to noise 28,125 , but such heterogeneity is a major reason that AI systems fail when trialled in the clinic 126 .”
Deep learning-enabled medical computer vision (2021, methodology): “This development, coupled with advances in CV has led to research and commercialization activity in AI-driven digital histopathology 75 .”
Deep learning in histopathology: the path to the clinic (2021, background): “The history of CPATH before deep learning has been reviewed elsewhere 13 , as has the application of deep learning for histopathology from a technical perspective 14 .”
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication (2021, background): “However, the deep learning based biopsy diagnosis in WSI has to face a great challenges due to the huge size and the lack of pixel-level annotations[5].”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, background): “Because they can be applied to raw data without ground truth labels, unsupervised methods are often useful to structure extremely large datasets 98–102 .”