lilygeorgescu/MHCA
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
This project helps medical professionals enhance low-resolution medical images, like MRI scans, to achieve clearer, higher-resolution versions. It takes one or more low-resolution medical images as input and produces a single, enhanced high-resolution image, which can aid in better diagnosis and analysis. This would be used by radiologists, medical imaging specialists, and researchers who work with medical image analysis.
No commits in the last 6 months.
Use this if you need to improve the clarity and detail of low-resolution medical images, especially T2-weighted MRI scans, for better diagnostic accuracy or research.
Not ideal if you are working with non-medical images or require super-resolution for modalities other than those tested, as performance may vary.
Stars
51
Forks
2
Language
Python
License
—
Category
Last pushed
Dec 13, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lilygeorgescu/MHCA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
open-mmlab/mmpretrain
OpenMMLab Pre-training Toolbox and Benchmark
facebookresearch/mmf
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
adambielski/siamese-triplet
Siamese and triplet networks with online pair/triplet mining in PyTorch
HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis
Papers, code and datasets about deep learning and multi-modal learning for video analysis
KaiyangZhou/pytorch-vsumm-reinforce
Unsupervised video summarization with deep reinforcement learning (AAAI'18)