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  3. Radgazegen: Radiomics And Gaze-guided Chest X-ray Generation Using Diffusion Models.
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  3. Radgazegen: Radiomics And Gaze-guided Chest X-ray Generation Using Diffusion Models.

Related Experiment Video

Acquiring Hyperpolarized 129Xe Magnetic Resonance Images of Lung Ventilation
09:08

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Published on: November 21, 2023

RadGazeGen: radiomics and gaze-guided chest X-ray generation using diffusion models.

Moinak Bhattacharya1, Gagandeep Singh2, Shubham Jain1

  • 1Stony Brook University, Stony Brook, New York, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

RadGazeGen integrates radiologists' eye gaze and radiomic features into AI image generation for improved medical accuracy. This framework enhances the clinical validity of synthesized medical images.

Keywords:
chest X-raysdiffusion modelsdisease classificationeye gazeradiomics

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Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Text-to-image diffusion models show promise but struggle with clinical accuracy and anatomical fidelity due to limitations of textual descriptions alone.
  • Generating clinically accurate medical images requires capturing subtle disease-specific details often missed by text-only prompts.

Purpose of the Study:

  • To introduce RadGazeGen, a novel framework for high-fidelity medical image generation.
  • To integrate expert eye gaze patterns and radiomic feature maps as controls in text-to-image diffusion models.
  • To enhance the anatomical consistency and disease-specific accuracy of generated medical images.

Main Methods:

  • RadGazeGen utilizes radiologists' eye gaze trajectories to encode visuo-cognitive attention and spatial localization of disease indicators.
  • Radiomic features are employed to capture subvisual phenotypic characteristics like texture, intensity, and shape.
  • These multimodal cues (gaze and radiomics) are used as spatial and semantic controls within the diffusion process.

    • Main Results:

    • The framework was evaluated on the REFLACX dataset for image generation quality and diversity.
    • Generated images demonstrated high fidelity and diagnostic relevance in downstream tasks, including disease classification on the CheXpert test set.
    • Performance was further assessed in long-tailed learning evaluations on the MIMIC-CXR-LT dataset.

    Conclusions:

    • RadGazeGen successfully bridges the gap between human visual cognition and machine perception in medical image synthesis.
    • Jointly conditioning on gaze and radiomic representations improves both the realism and clinical validity of generated medical images.
    • The study highlights the importance of anatomically grounded and disease-aware controls for advanced diffusion-based medical image generation.