McCaD
importedsoftware/mccad
[WACV2025, Early Accepted] McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/sanuwanihewa/McCaD
- Documentation
- unknown
- Tags
- adversarial · diffusion · gan · medical-imaging · mri · multi-contrast · pytorch · synthesis
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- MU-Diffadversarial · diffusion · gan · medical-imaging · mri · multi-contrast · pytorch · synthesis
Official Pytorch implementation for MU-Diff: A Mutual Learning Diffusion Model for Synthetic MRI with Application for Brain lesions
- D2Diffdiffusion · mri · multi-contrast · pytorch
Official Pytorch Implementation for D2Diff: A Dual-Domain Diffusion Model for Accurate Multi-Contrast MRI Synthesis [Accepted at MICCAI2025]
- PatchBased_3DCycleGAN_CT_Synthesisgan · medical-imaging · synthesis
Patch-based 3D Cycle-GAN for volumetric medical image synthesis
- ccnetmedical-imaging · mri · synthesis
Official repository of "Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models"
- WaveDiTdiffusion · medical-imaging · mri
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis - Accepted at MICCAI 2026
- TC-MGANgan · mri · pytorch
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis
- api.github.com/repos/sanuwanihewa/McCaDretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2024-09-09, 10 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/19.json→ .entries["mccad"]
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