Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models

Documenti analoghi

• Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
  di: Rougé, Pierre, et al.
  Pubblicazione: (2023)
• XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation
  di: Ko, Byunghyun, et al.
  Pubblicazione: (2025)
• From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task
  di: Chen, Bohao, et al.
  Pubblicazione: (2024)
• SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution
  di: Forigua, Cristhian, et al.
  Pubblicazione: (2024)
• 3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
  di: Niu, Chuang, et al.
  Pubblicazione: (2024)