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Deep neural network models for computational histopathology: A survey.

Chetan L Srinidhi1, Ozan Ciga2, Anne L Martel1

  • 1Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Canada.

Medical Image Analysis
|October 13, 2020
PubMed
Summary

Deep learning methods are revolutionizing histopathology image analysis for disease understanding and prognosis. This review covers supervised, unsupervised, and transfer learning approaches, highlighting challenges and future research directions in computational pathology.

Keywords:
Computational histopathologyConvolutional neural networksDeep learningDigital pathologyHistology image analysisReviewSurvey

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Area of Science:

  • Computational pathology
  • Medical image analysis
  • Artificial intelligence in healthcare

Background:

  • Histopathological images offer crucial phenotypic data for disease progression and survival analysis.
  • Deep learning (DL) is now the predominant methodology for interpreting complex histology data.
  • Analyzing these images aids in understanding disease mechanisms and predicting patient outcomes.

Purpose of the Study:

  • To provide a comprehensive review of state-of-the-art deep learning techniques in histopathological image analysis.
  • To categorize and discuss various machine learning strategies, including supervised, weakly supervised, and unsupervised learning.
  • To explore deep learning-based survival models for disease prognosis and identify future research directions.

Main Methods:

  • Systematic survey of over 130 research papers on deep learning in histopathology.
  • Categorization of methods based on machine learning strategies (supervised, weakly supervised, unsupervised, transfer learning).
  • Review of deep learning applications in survival analysis and prognosis.

Main Results:

  • Identified diverse deep learning methodologies applied to histopathology, including various sub-variants.
  • Highlighted the progress and trends in applying machine learning to histology image interpretation.
  • Summarized existing open datasets and discussed the capabilities of DL-based survival models.

Conclusions:

  • Deep learning significantly advances histopathological image analysis for disease monitoring and prognosis.
  • Further research is needed to address current challenges and limitations in DL approaches for computational pathology.
  • Open datasets and advanced DL models are crucial for future progress in personalized medicine and diagnostics.