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Updated: Dec 13, 2025

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Deep learning in digital pathology image analysis: a survey.

Shujian Deng1,2,3, Xin Zhang1,2,3, Wen Yan1,2,3

  • 1School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.

Frontiers of Medicine
|July 31, 2020
PubMed
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This summary is machine-generated.

Deep learning (DL) excels in digital pathology image analysis, automating feature extraction for tasks like classification and segmentation. This review highlights DL

Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical image analysis

Background:

  • Traditional digital pathology analysis relies on manual feature engineering.
  • Deep learning (DL) offers automated feature representation learning.
  • DL methods reduce labor intensity in feature extraction compared to conventional machine learning.

Purpose of the Study:

  • To comprehensively review recent deep learning-based image analysis studies in histopathology.
  • To cover diverse tasks including classification, segmentation, and detection.
  • To explore various applications such as stain normalization and structural analysis.

Main Methods:

  • Literature review of recent studies utilizing deep learning for histopathology image analysis.
  • Categorization of studies by analysis task (classification, segmentation, detection) and application area.
Keywords:
classificationdeep learningdetectionpathologysegmentation

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  • Synthesis of findings on the performance and utility of DL methods.

    • Main Results:

    • Deep learning achieves state-of-the-art performance in numerous digital pathology tasks.
    • DL methods learn representations without manual feature design, reducing labor.
    • DL approaches provide consistent and accurate outcomes in image analysis.

    Conclusions:

    • Deep learning is a powerful and promising tool for digital pathology.
    • DL can significantly assist pathologists in clinical diagnosis.
    • Automated analysis via DL enhances efficiency and accuracy in histopathology.