The field of medical imaging is witnessing significant advancements with the integration of generative AI techniques. Researchers are leveraging these methods to address long-standing challenges such as data scarcity, image quality, and diagnostic accuracy. Notably, diffusion models and generative adversarial networks (GANs) are being explored for their potential to generate synthetic images that can augment training datasets, enhance diagnostic models, and improve image reconstruction. These innovative approaches are paving the way for more accurate and reliable medical imaging, with potential applications in various clinical settings.

Some noteworthy papers in this area include: Improved Sub-Visible Particle Classification in Flow Imaging Microscopy via Generative AI-Based Image Synthesis, which demonstrates the effectiveness of diffusion models in generating high-fidelity images for particle classification. Lung-DDPM+ achieves efficient thoracic CT image synthesis using a denoising diffusion probabilistic model, showcasing its potential for broader applications in medical imaging. MInDI-3D presents a 3D conditional diffusion-based model for sparse-view Cone Beam Computed Tomography artefact removal, enabling reduced imaging radiation exposure.