https://github.com/icon-lab/transms
Official Implementation of Transformers for System Matrix Super-resolution (TranSMS)
https://github.com/icon-lab/transms
deep-learning image-processing medical-imaging super-resolution transformers
Last synced: about 1 year ago
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Official Implementation of Transformers for System Matrix Super-resolution (TranSMS)
- Host: GitHub
- URL: https://github.com/icon-lab/transms
- Owner: icon-lab
- Created: 2022-07-07T11:27:50.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-11T21:08:39.000Z (about 3 years ago)
- Last Synced: 2023-12-13T22:11:12.693Z (over 2 years ago)
- Topics: deep-learning, image-processing, medical-imaging, super-resolution, transformers
- Language: Python
- Homepage:
- Size: 103 KB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# TranSMS
Official Implementation of Transformers for System Matrix Super-resolution (TranSMS)
A. Güngör, B. Askin, D. A. Soydan, E. U. Saritas, C. B. Top and T. Çukur, "TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging," in IEEE Transactions on Medical Imaging, 2022, doi: 10.1109/TMI.2022.3189693.
# Demo
You can use the following links to download training, validation, test datasets.
# Dataset
https://drive.google.com/drive/folders/1MvLouN4Jl1gg-Uj3PMYrq2sIw-bw_HeF?usp=sharing
# Pretrained Networks:
https://drive.google.com/drive/folders/1LkElq3pQS_G_mu56lCwFgvwFysknrNXU?usp=share_link
# Training
Generic training code code:
```python checkTranSMSAselFFLTrain.py --useGPUno 0 --wd 0 --lr 1e-4 --scale_factor 2 --snrThreshold 5 --useNoisyProjection 1 --bs 64 --resultFolder . --n1 32 --n2 32 --trainFolder ./train --testFolder ./val```
```useGPUno: Selected GPU
wd: weight decay, default is 0
lr: learning rate
scale_factor: 2, 4, 8, etc.
snrThreshold: SNR threshold for SM training, i.e. values below threshold are not used for training
useNoisyProjection: 0 ablates the data consistency block from TranSMS, 1 is regular TranSMS with data consistency
bs: batch size
resultFolder: path for saving model outputs
n1: SM dimension x
n2: SM dimension y
trainFolder: folder containing training SMs
testFolder: folder containing validation SMs
```
# Code for Open MPI dataset
Code for 2x, 4x and 8x training using Open MPI dataset:
```python checkTranSMSAselFFLTrain.py --useGPUno 0 --lr 5e-4 --scale_factor 2 --resultFolder . --trainFolder ./train --testFolder ./val```
```python checkTranSMSAselFFLTrain.py --useGPUno 0 --lr 1e-4 --scale_factor 4 --resultFolder . --trainFolder ./train --testFolder ./val```
```python checkTranSMSAselFFLTrain.py --useGPUno 0 --lr 5e-5 --scale_factor 8 --resultFolder . --trainFolder ./train --testFolder ./val```
# Inference
Code for inference using all trained networks:
```python inferenceOnOpenMPI.py --useGPUno 0 --bs 256 --n1 32 --n2 32 --modelFolder ./outs/ --saveOutFolder ./results/ --testFolder ./test --interpolationMatrixPath interpolaters.mat```
```useGPUno: Selected GPU
bs: batch size during inference
n1: SM dimension x
n2: SM dimension y
modelFolder: folder containing trained networks
saveOutFolder: path for saving "mat" file outputs
testFolder: folder containing test SMs
interpolationMatrixPath: path containing the interpolation matrix from 4x4, 8x8, 16x16 to 32x32, for fast interpolation purposes
```
**************************************************************************************************************************************
# Citation
You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.
```
@article{transms,
author={Gungor, Alper and Askin, Baris and Soydan, Damla Alptekin and Saritas, Emine Ulku and Top, Can Baris; and Cukur, Tolga},
journal={IEEE Transactions on Medical Imaging},
title={TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TMI.2022.3189693}}
```
(c) ICON Lab 2022
# Prerequisites
- Python 3.6
- CuDNN 8.2.1
- PyTorch 1.10.0
# Acknowledgements
This code uses libraries from CvT, SRCNN and VDSR repositories.
For questions/comments please send an email to: alperg@ee.bilkent.edu.tr