https://github.com/icon-lab/denomamba
Official implementation of DenoMamba: A fused state-space model for low-dose CT denoising
https://github.com/icon-lab/denomamba
computed-tomography contextualized-representation deep-learning denoising long-range low-dose-ct-denoising mamba medical-imaging python pytorch sequence-models state-space-models
Last synced: 4 months ago
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Official implementation of DenoMamba: A fused state-space model for low-dose CT denoising
- Host: GitHub
- URL: https://github.com/icon-lab/denomamba
- Owner: icon-lab
- License: mit
- Created: 2024-09-20T05:56:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-30T06:13:12.000Z (9 months ago)
- Last Synced: 2025-06-25T15:51:30.614Z (8 months ago)
- Topics: computed-tomography, contextualized-representation, deep-learning, denoising, long-range, low-dose-ct-denoising, mamba, medical-imaging, python, pytorch, sequence-models, state-space-models
- Language: Python
- Homepage:
- Size: 368 KB
- Stars: 28
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
DenoMamba
A fused state-space model for low-dose CT denoising
[arXiv]
Official PyTorch implementation of **DenoMamba**, a novel denoising method based on state-space modeling (SSM), that efficiently captures short- and long-range context in medical images. Following an hourglass architecture with encoder-decoder stages, DenoMamba employs a spatial SSM module to encode spatial context and a novel channel SSM module equipped with a secondary gated convolution network to encode latent features of channel context at each stage. Feature maps from the two modules are then consolidated with low-level input features via a convolution fusion module (CFM).
## Dependencies
```
python>=3.8.13
cuda=>11.6
torch>=2.2
torchvision>=0.17
pillow
scipy
mamba-ssm==1.1.3
```
You are welcome to ask any library issues.
## Dataset
To reproduce the results reported in the paper, we recommend the following dataset processing steps:
Sequentially select subjects from the dataset.
Select 2D cross-sections from each subject.
Normalize the selected 2D cross-sections before training and before metric calculation.
You should structure your aligned dataset in the following way:
```
/datasets/LDCT/
├── Full_Dose ├── Quarter_Dose
│ ├── 1mm │ ├── 1mm
│ ├── Sharp Kernel │ ├── Sharp Kernel
│ ├── L506 │ ├── L506
| - ... | - ...
│ ├── ... │ ├── ...
│ ├── Soft Kernel │ ├── Soft Kernel
│ ├── L506 │ ├── L506
| - ... | - ...
│ ├── ... │ ├── ...
│ ├── 3mm │ ├── 3mm
│ ├── Sharp Kernel │ ├── Sharp Kernel
│ ├── L192 │ ├── L192
| - ... | - ...
│ ├── ... │ ├── ...
│ ├── Soft Kernel │ ├── Soft Kernel
│ ├── L192 │ ├── L192
| - ... | - ...
│ ├── ... │ ├── ...
```
## Training
### Example Command for training with default arguments
```
python3 train.py --full_dose_path example_full_dose_path --quarter_dose_path example_quarter_dose_path --path_to_save example_path_to_save_the_trained_model
```
### Argument Descriptions
| Argument | Description |
|---------------------|---------------------------------------------------------------|
| --full_dose_path | Path to full dose images |
| --quarter_dose_path | Path to quarter dose images |
| --dataset_ratio | The ratio of dataset to use (in case of big dataset) |
| --train_ratio | Ratio of train dataset to all dataset |
| --batch_size | Batch size. |
| --in_ch | Number of input image channels |
| --out_ch | Number of output image channels |
| --learning_rate | Learning rate |
| --max_epoch | Maximum number of epochs |
| --continue_to_train | Continue any interrupted training |
| --path_to_save | Path to save the trained model |
| --ckpt_path | Path to trained and saved checkpoint model |
| --validation_freq | Frequency to run validation |
| --save_freq | Frequency to save model |
| --batch_number | Number of a batch in validation to show the sample images |
| --num_blocks | Number of FuseSSM block layers |
| --dim | Number of FuseSSM blocks |
| --num_refinement_blocks | Number of refinement blocks |
#### Key Required Arguments
```
--full_dose_path: Must specify the path to the full-dose dataset.
--quarter_dose_path: Must provide the path to the quarter-dose dataset.
--path_to_save: Required to specify where the trained model will be saved.
```
These arguments are essential for the program to locate the necessary datasets and save the trained model.
## Test
### Example Command for test with default arguments
```
python test.py --full_dose_path example_full_dose_path --quarter_dose_path example_quarter_dose_path --path_to_save example_path_to_save_the_trained_model --output_root example_path_to_save_the_output_images
```
### Argument Descriptions
| Argument | Description |
|---------------------|---------------------------------------------------------------|
| --full_dose_path | Path to full dose images |
| --quarter_dose_path | Path to quarter dose images |
| --dataset_ratio | The ratio of dataset to use (in case of big dataset) |
| --train_ratio | Ratio of train dataset to all dataset |
| --batch_size | Batch size. |
| --in_ch | Number of input image channels |
| --out_ch | Number of output image channels |
| --ckpt_path | Path to trained and saved checkpoint model |
| --output_root | Path to save denoised images |
| --num_blocks | Number of FuseSSM block layers |
| --dim | Number of FuseSSM blocks |
| --num_refinement_blocks | Number of FuseSSM blocks |
#### Key Required Arguments
```
--full_dose_path: Must specify the path to the full-dose dataset.
--quarter_dose_path: Must provide the path to the quarter-dose dataset.
--ckpt_path: Required to specify where the trained model was saved.
--output_root : Required to specify where to save the output images.
```
These arguments are essential for the program to locate the necessary datasets and save the trained model.
## Citation
You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.
```
@article{ozturk2024denomamba,
title={DenoMamba: A fused state-space model for low-dose CT denoising},
author={Şaban Öztürk and Oğuz Can Duran and Tolga Çukur},
year={2024},
journal={arXiv:2409.13094}
}
```
For any questions, comments and contributions, please feel free to contact Şaban Öztürk (saban.ozturk[at]bilkent.edu.tr)
## Acknowledgments
This code uses libraries from [Restormer](https://github.com/swz30/Restormer) and [mamba](https://github.com/state-spaces/mamba) repository.
Copyright © 2024, ICON Lab.