MedSyn2 generates controllable high-resolution 3D CT volumes using optional text prompts and partial semantic segmentation masks via a modified diffusion transformer with gated attention.
arXiv:2409.11169v2
6 Pith papers cite this work. Polarity classification is still indexing.
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iTRIALSPACE generates realistic virtual lesion trials on lung CTs that isolate performance drivers and show strong transfer of model rankings to real clinical data (ρ=0.93).
A sequential diffusion framework generates controllable abdominal anatomies with a Volume Control Scalar that decouples organ size from body habitus, achieving Dice scores around 0.83 and reducing distributional mismatch by 73.6% in a hepatomegaly example.
A foundation VAE pretrained on natural images and videos serves as a frozen interface for CT reconstruction, augmentation, and generation, yielding 3.9% NSD gains in segmentation and improved generation metrics across 18 diseases.
3DGR-CT adapts 3D Gaussian splatting with FBP-guided initialization and differentiable CT projection for sparse-view reconstruction, claiming better accuracy and speed than prior methods.
2D diffusion-generated synthetic X-rays enable training of anatomical landmark detectors that generalize to real images with performance rivaling real-data training.
citing papers explorer
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3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation
3DGR-CT adapts 3D Gaussian splatting with FBP-guided initialization and differentiable CT projection for sparse-view reconstruction, claiming better accuracy and speed than prior methods.
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2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
2D diffusion-generated synthetic X-rays enable training of anatomical landmark detectors that generalize to real images with performance rivaling real-data training.