EchoDiffusion
importeddata/echodiffusion
MICCAI 2023 code for the paper: Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. EchoDiffusion is a collection of video diffusion models trained from scratch…
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Data & Standards
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Repository
- github.com/HReynaud/EchoDiffusion
- Documentation
- unknown
- Tags
- cardiac · deep-learning · diffusion · medical-imaging · simulation · ultrasound · video
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- wurb_2026ultrasound · video
CloudedBats WURB-2026, the DIY ultrasonic sound detector for bat monitoring.
- aladdin_cmr_lacardiac · medical-imaging
Source code for Aladdin, a complete workflow for 3D MRI left atrium motion analysis
- McCaDdiffusion · medical-imaging
[WACV2025, Early Accepted] McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
- MU-Diffdiffusion · medical-imaging
Official Pytorch implementation for MU-Diff: A Mutual Learning Diffusion Model for Synthetic MRI with Application for Brain lesions
- WaveDiTdiffusion · medical-imaging
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis - Accepted at MICCAI 2026
- CineMAcardiac
A Vision Foundation Model for Cine Cardiac Magnetic Resonance Imaging
- api.github.com/repos/HReynaud/EchoDiffusionretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2024-10-29, 74 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
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/v1/entries/23.json→ .entries["echodiffusion"]
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