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Area of Science:

  • Digital pathology and computational analysis of microscopic images.
  • Artificial intelligence (AI) in medical diagnostics and pathology.
  • Biomedical image analysis and machine learning applications.

Background:

  • Deep learning (DL) models excel in histopathological slide analysis but lack interpretability.
  • Handcrafted features from biological objects (nuclei, cells, tissues) offer better explainability.
  • Machine learning (ML) with handcrafted features can complement DL for pathologist assistance.

Purpose of the Study:

  • To systematically review the use of biological features in hematoxylin & eosin (H&E) microscopic images for medical questions.
  • To identify feature categories, data sources, and medical applications in published literature.
  • To assess methodological limitations and identify promising avenues for explainable AI in pathology.

Main Methods:

  • Systematic literature review adhering to PRISMA guidelines.
  • Analysis of 97 articles published between January 2005 and May 2025.
  • Data extraction from PubMed, IEEE, and ACM databases focusing on feature types, sources, and medical questions.

Main Results:

  • Identified three primary feature categories: texture/color, morphology, and topology.
  • Features most commonly derived from segmented cells (80 studies) and tissues (28 studies).
  • Features applied to seven medical questions: normal vs. diseased, subtyping, grading, phenotyping, object detection, prognosis, and treatment response.

Conclusions:

  • Methodological limitations include interpretability challenges, data leakage, and small sample sizes.
  • Domain-inspired feature engineering enhances explainability and specificity.
  • Increased methodological rigor in feature engineering can improve AI model relevance and reliability in pathology.