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WaveDiT

imported

software/wavedit

WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis - Accepted at MICCAI 2026

Machine-generated from the listed sources and not yet reviewed by a human.

WaveDiT project image
GitHub preview card for sisinflab/WaveDiT. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
sisinflab
Country
unknown
Documentation
unknown
Tags
deep-learning · diffusion · flow-matching · generative-3d · generative-ai · medical-imaging · miccai · mri · mri-brain · neuroimaging · python · transformers
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • meta-flow-matchingdiffusion · flow-matching

    Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

  • MOTFMflow-matching · medical-imaging · mri

    Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality

  • McCaDdiffusion · medical-imaging · mri

    [WACV2025, Early Accepted] McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis

  • MU-Diffdiffusion · medical-imaging · mri

    Official Pytorch implementation for MU-Diff: A Mutual Learning Diffusion Model for Synthetic MRI with Application for Brain lesions

  • MICAFlowmri · mri-brain · neuroimaging

    The easy way to process MRI data

  • Perfusion-NOBELmri · mri-brain · neuroimaging

    Pre-clinical DSC-MRI perfusion map generator written in Python

sources
  1. api.github.com/repos/sisinflab/WaveDiT
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2026-08-07, 8 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 →

machine-readable

/v1/entries/29.json→ .entries["wavedit"]

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