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October 20, 2025Open Access

Cycle Diffusion Model for Counterfactual Image Generation

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Cycle training improves conditioning adherence and image realism in 3D brain MRI datasets, suggesting enhanced counterfactual generation.

Enhanced image quality is achieved through the cycle diffusion model, improving conditioning accuracy for medical imaging.
The cycle training framework outperforms previous methods, resulting in better synthetic images measured by FID and SSIM.
Experiments on 3D brain MRI datasets reveal that the proposed model supports reliable direct and counterfactual image generation.
This approach highlights the potential for refining medical image synthesis, specifically in data augmentation and disease modeling.

Huang et al. (2025) studied this question.

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October 20, 2025Open Access

View Full Paper

Ask AI

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Authors

Cycle training improves conditioning adherence and image realism in 3D brain MRI datasets, suggesting enhanced counterfactual generation.

Enhanced image quality is achieved through the cycle diffusion model, improving conditioning accuracy for medical imaging.
The cycle training framework outperforms previous methods, resulting in better synthetic images measured by FID and SSIM.
Experiments on 3D brain MRI datasets reveal that the proposed model supports reliable direct and counterfactual image generation.
This approach highlights the potential for refining medical image synthesis, specifically in data augmentation and disease modeling.

Huang et al. (2025) studied this question.

View Full Paper

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