Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models
This work addresses data scarcity and privacy concerns in gynaecological imaging, providing a resource for training diagnostic models, though it is incremental as it applies existing diffusion models to a specific domain.
The paper tackled the challenge of generating anatomically precise synthetic uterine MRI images to address data scarcity and privacy issues in gynaecological imaging, resulting in high-fidelity images that improved diagnostic accuracy in a classification task.
Despite significant progress in generative modelling, existing diffusion models often struggle to produce anatomically precise female pelvic images, limiting their application in gynaecological imaging, where data scarcity and patient privacy concerns are critical. To overcome these barriers, we introduce a novel diffusion-based framework for uterine MRI synthesis, integrating both unconditional and conditioned Denoising Diffusion Probabilistic Models (DDPMs) and Latent Diffusion Models (LDMs) in 2D and 3D. Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans and provide valuable resources for training robust diagnostic models. We evaluate generative quality using advanced perceptual and distributional metrics, benchmarking against standard reconstruction methods, and demonstrate substantial gains in diagnostic accuracy on a key classification task. A blinded expert evaluation further validates the clinical realism of our synthetic images. We release our models with privacy safeguards and a comprehensive synthetic uterine MRI dataset to support reproducible research and advance equitable AI in gynaecology.