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Machine learning methods for histopathological image analysis: Updates in 2024.

Daisuke Komura1, Mieko Ochi1, Shumpei Ishikawa1

  • 1Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Computational and Structural Biotechnology Journal
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and digital pathology are revolutionizing healthcare. This review details machine learning advancements in histopathological image analysis since 2018, addressing key challenges and future trends.

Keywords:
Computer-assisted diagnosisDeep learningDigital image analysisFoundation modelHistopathologyMachine learningWhole slide image

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Histopathology

Background:

  • Artificial intelligence (AI) and digital pathology represent a paradigm shift in healthcare and biomedical research.
  • This review serves as an update to a 2018 publication, focusing on machine learning (ML) applications in histopathological image analysis.
  • Significant progress has been made in expanding the technical capabilities and practical applications of computational pathology.

Purpose of the Study:

  • To provide a comprehensive analysis of machine learning applications in histopathological image analysis, focusing on developments since 2018.
  • To highlight advances in addressing key challenges such as processing gigapixel whole slide images, data scarcity, multidimensional analysis, domain shifts, and model interpretability.
  • To evaluate emerging trends like foundation models and multimodal integration that are reshaping the field.

Main Methods:

  • Comprehensive literature review of machine learning applications in digital pathology.
  • Analysis of advancements in addressing specific technical and practical challenges.
  • Evaluation of emerging trends and their impact on the field.

Main Results:

  • Significant progress has been achieved in processing large-scale histopathological images (gigapixel whole slide images).
  • Methods for overcoming insufficient labeled data and domain shifts across institutions have been advanced.
  • Emerging trends like foundation models and multimodal integration show promise for future applications.
  • Improved interpretability of machine learning models in pathology is an ongoing area of development.

Conclusions:

  • Machine learning holds substantial potential to enhance routine pathological analysis and accelerate scientific discovery.
  • The field of computational pathology is rapidly evolving, driven by AI innovations.
  • This review offers guidance for researchers and clinicians on the current landscape and future directions of pathology image analysis.