https://github.com/Coradlut/Bi-JROS
The code for Bi-JROS.
https://github.com/Coradlut/Bi-JROS
Last synced: 5 months ago
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The code for Bi-JROS.
- Host: GitHub
- URL: https://github.com/Coradlut/Bi-JROS
- Owner: Coradlut
- Created: 2024-03-27T14:52:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-12T11:22:59.000Z (7 months ago)
- Last Synced: 2024-12-12T12:27:58.301Z (7 months ago)
- Language: Python
- Size: 2.5 MB
- Stars: 15
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Bi-JROS: Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation
This is the source code of paper "[Bi-JROS: Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation
].
## REQUIREMENTS
This code requires the following:
* Python==3.8
* PyTorch==1.12.1
* Torchvision==0.13.1
* Torchaudio==0.12.1
* Numpy==1.24.3
* Scipy==1.10.1
* Scikit-image==0.21.0
* Nibabel==5.2.0
## DATA
The datasets used in the paper, ABIDE, ANDI, PPMI, and OASIS, are publicly available for download.
For example, ADNI can be applied for and downloaded through the following link: [https://adni.loni.usc.edu/data-samples/adni-data/#AccessData](https://adni.loni.usc.edu/data-samples/adni-data/#AccessData).
The download process for ABIDE is described at [https://fcon_1000.projects.nitrc.org/indi/abide/databases.html](https://fcon_1000.projects.nitrc.org/indi/abide/databases.html).
Preprocessed ABIDE data can be accessed at [http://preprocessed-connectomes-project.org/abide/index.html](http://preprocessed-connectomes-project.org/abide/index.html).
## USAGE
### Step 1: Getting Started
Clone the repo:
```
git clone https://github.com/Coradlut/Bi-JROS.git
```
### Step 2: Training
```
python train.py
```
Before executing the code, it may be necessary to configure certain parameters in accordance with specific requirements.
### Step 3: Prediction
To test the performance:
```
python infer.py
```