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Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs:

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Summary

This review addresses deep learning challenges in breast cancer whole slide image analysis. It proposes a framework to improve accuracy, efficiency, and interpretability for better early detection.

Keywords:
breast cancerdeep learningdetection algorithmsystematic literature reviewwhole slide images

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Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Oncology
  • Biomedical Imaging Analysis

Background:

  • Breast cancer remains a leading global health concern for women.
  • Accurate detection of lymphocytes and biomarkers in whole slide images (WSIs) is crucial for prognosis.
  • Deep learning on WSIs faces challenges: image variability, annotation scarcity, interpretability, and computational demands.

Purpose of the Study:

  • To systematically review deep learning methods for breast cancer detection in WSIs.
  • To introduce a novel five-dimensional evaluation framework for assessing these methods.
  • To provide a roadmap for overcoming current challenges in WSI-based breast cancer diagnostics.

Main Methods:

  • Systematic literature review following PRISMA guidelines.
  • Analysis of 39 peer-reviewed studies and 20 WSI datasets (2020-2024).
  • Development of a five-dimensional evaluation framework: accuracy, robustness, interpretability, efficiency, and annotation quality.

Main Results:

  • Identified key challenges and innovations in deep learning for WSI breast cancer analysis.
  • The proposed framework enables balanced assessment of deep learning models.
  • Highlights the need for improved annotation quality and model interpretability.

Conclusions:

  • The review offers a comprehensive analysis of current deep learning approaches for WSI breast cancer detection.
  • A practical roadmap is proposed to address persistent challenges.
  • Provides actionable guidance for optimizing and translating WSI technologies into clinical practice.