387-diffusion-adversarial-representation-learning-for-self-supervised-vessel-segmentation
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Basic Info
- Host: GitHub
- Owner: SZU-AdvTech-2023
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 3.84 MB
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Created over 2 years ago
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Metadata Files
Citation
https://github.com/SZU-AdvTech-2023/387-Diffusion-Adversarial-Representation-Learning-for-Self-Supervised-Vessel-Segmentation/blob/main/
# Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
This repository is the official implementation of "Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation".
[[ICLR 2023](https://openreview.net/forum?id=H0gdPxSwkPb)]
[[arXiv](https://arxiv.org/abs/2209.14566)]
## Requirements
* OS : Ubuntu
* Python >= 3.6
* PyTorch >= 1.4.0
## Data
In our experiments, we used the publicly available XCAD dataset. Please refer to our main paper.
## Training
To train our model, run this command:
```train
python3 main.py -p train -c config/train.json
```
## Test
To test the trained our model, run:
```eval
python3 main.py -p test -c config/test.json
```
## Pre-trained Models
You can download our pretrained model of XCAD dataset [here](https://drive.google.com/file/d/1Kuh-YEhRaR4LEsltnXflnJgxSoTx06j5/view?usp=sharing).
Then, you can test the model by saving the pretrained weights in the directory ./pretrained_model.
To brifely test our method given the pretrained model, we provided the toy example in the directory './data/'.
## Citations
```
@inproceedings{
kim2023diffusion,
title={Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation},
author={Boah Kim and Yujin Oh and Jong Chul Ye},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=H0gdPxSwkPb}
}
```
Owner
- Name: SZU-AdvTech-2023
- Login: SZU-AdvTech-2023
- Kind: organization
- Repositories: 1
- Profile: https://github.com/SZU-AdvTech-2023