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Updated: May 7, 2026

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Medical hierarchical image classification via dual-geometry image-text learning.

Lei Fan1, Arcot Sowmya2, Erik Meijering2

  • 1Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Australia; School of Computer Science and Engineering, UNSW Sydney, Australia.

Medical Image Analysis
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

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Medical image analysis·2026

This study introduces H2CL, a novel dual-geometry framework for hierarchical image classification in medical analysis. It effectively combines Euclidean and hyperbolic features, significantly improving classification accuracy across diverse datasets.

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Machine Learning

Background:

  • Hierarchical image classification is crucial for medical image analysis, reflecting biological and clinical relationships.
  • Current methods often involve complex model designs and training strategies for multi-task learning and fine-grained detection.

Purpose of the Study:

  • To exploit the negative curvature of hyperbolic space for efficient representation of hierarchical structures in medical images.
  • To propose a novel dual-geometry image-text framework (H2CL) for improved hierarchical image classification.

Main Methods:

  • Developed H2CL, a framework utilizing a lightweight classifier head to extract both Euclidean and hyperbolic features from images.
  • Integrated a text branch with entailment loss to model image-text alignment and inter-sample relationships.
Keywords:
Contrastive learningHierarchical classificationHyperbolic space

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  • Employed hyperbolic space properties for efficient representation of hierarchical data.

    • Main Results:

    • H2CL consistently outperformed advanced methods on cervical cell, skin lesion, and gallbladder disease datasets.
    • Achieved an average accuracy improvement of 7% at the fine-grained level compared to standard Swin Transformer.
    • Demonstrated consistent performance gains when integrated with various backbone models.

    Conclusions:

    • The proposed H2CL framework effectively leverages dual-geometry representations for enhanced hierarchical image classification.
    • The integration of image and text features, guided by hyperbolic geometry, offers a promising direction for medical image analysis.
    • H2CL provides a robust and efficient solution for complex medical image classification tasks.