lyhkevin/MT-Net
Multi-scale Transformer Network for Cross-Modality MR Image Synthesis (IEEE TMI)
This project helps medical professionals generate missing or alternative types of MRI images from existing ones. By inputting one type of MRI scan (e.g., T1-weighted), it can produce a synthetic version of another type (e.g., T2-weighted). This is useful for radiologists, neuroradiologists, or researchers working with brain imaging who need diverse image modalities for diagnosis or analysis, even when not all scans are available.
No commits in the last 6 months.
Use this if you need to synthesize different MRI modalities from your existing scans, especially when working with brain imaging and you have limited access to specific scan types.
Not ideal if you are looking to synthesize images for medical imaging types other than MRIs, or if your primary goal is not cross-modality image generation.
Stars
40
Forks
2
Language
Python
License
—
Category
Last pushed
Dec 24, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lyhkevin/MT-Net"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022