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TagGAN: A generative model for data tagging.

Muhammad Nawaz1, Basma Nasir2, Tehseen Zia3

  • 1Data Science Institute, University of Technology Sydney, Australia; Medical Imaging and Diagnostics Lab, National Center of Artificial Intelligence, Pakistan.

Computers in Biology and Medicine
|December 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

TagGAN, a novel Generative Adversarial Networks (GANs) framework, generates pixel-level disease maps from image-level labels for medical image analysis. This weakly-supervised approach enhances AI interpretability and assists radiologists by automating mask generation.

Keywords:
COVID-19Data taggingExplainable artificial intelligenceGenerative adversarial networksTuberculosisWeakly supervised learning

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Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence in Healthcare
  • Computer Vision

Background:

  • Accurate pixel-level disease identification is crucial for diagnosis and monitoring.
  • Conventional AI methods struggle with limited pixel-level annotations and lack transparency.
  • Existing techniques require binary masks, which are often unavailable.

Purpose of the Study:

  • To develop a weakly-supervised framework for fine-grained disease map generation using only image-level labels.
  • To enhance the interpretability of diagnostic AI through precise disease lesion visualization.
  • To automate binary mask generation for radiologist assistance.

Main Methods:

  • Proposed TagGAN, a Generative Adversarial Networks (GANs)-based framework for weakly-supervised disease map generation.
  • Employed domain translation to generate pixel-level disease maps from abnormal to normal image representations.
  • Subtracted generated disease maps from abnormal images to create normal counterparts, preserving anatomical details.

    • Main Results:

    • TagGAN successfully generated fine-grained disease maps without requiring pixel-level annotations.
    • The framework demonstrated enhanced interpretability by visualizing disease-specific regions.
    • Achieved state-of-the-art performance, outperforming existing methods by over 6% in identifying disease-specific pixels on benchmark datasets (CheXpert, TBX11K, COVID-19).

    Conclusions:

    • TagGAN offers a robust solution for weakly-supervised disease map generation in medical imaging.
    • The model significantly improves the accuracy of disease localization and enhances AI transparency.
    • TagGAN reduces radiologist workload by eliminating the need for manual binary mask annotation during training.