# SSL

**Repository Path**: peng-yu/ssl

## Basic Information

- **Project Name**: SSL
- **Description**: 保存SSL代码
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-20
- **Last Updated**: 2024-03-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [IJCAI 2023] UCMT

This repo is the PyTorch implementation of our paper:

**["Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation"](https://www.ijcai.org/proceedings/2023/467)** 

<!-- ![framework](docs/UCMT.png) -->
<img src=docs/UCMT.png width=75% />

**U**ncertainty-guided **C**ollaborative **M**ean-**T**eacher (UCMT)

## Usage

🔥🔥 the 3D version of UCMT has been uploaded. 🔥🔥

### 0. Requirements
The code is developed using Python 3.7 with PyTorch 1.11.0. 
All experiments in our paper were conducted on a single NVIDIA Quadro RTX 6000 with 24G GPU memory.

Install from the ```requirements.txt``` using:
```angular2html
pip install -r requirements.txt
```

### 1. Data Preparation
#### 1.1. Download data
The original data can be downloaded in following links:
* ISIC Dataset - [Link (Original)](https://challenge.isic-archive.com/data/#2018)

#### 1.2. Split Dataset
The ISIC dataset includes 2594 dermoscopy images and corresponding annotations. 
Split the dataset, resulting in 1815 images for training and 779 images for testing.
```angular2html
python data/split_dataset.py
```
Then, the dataset is arranged in the following format:
```
DATA/
|-- ISIC
|   |-- TrainDataset
|   |   |-- images
|   |   |-- masks
|   |-- TestDataset
|   |   |-- images
|   |   |-- masks
```

### 2. Training
#### 2.1 Adopting DeepLabv3Plus as backbone:
```angular2html
python train.py --backbone DeepLabv3p
```
#### 2.2 Adopting U-Net as backbone:
```angular2html
python train.py --backbone UNet
```

### 3. Evaluation
```angular2html
python eval.py
```

### 4. Visualization
```angular2html
python visualization.py
```


## Citation
If you find this project useful, please consider citing:
```
@inproceedings{ijcai2023p467,
  title     = {Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation},
  author    = {Shen, Zhiqiang and Cao, Peng and Yang, Hua and Liu, Xiaoli and Yang, Jinzhu and Zaiane, Osmar R.},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {4199--4207},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/467},
  url       = {https://doi.org/10.24963/ijcai.2023/467},
}
```

## Contact
If you have any questions or suggestions, please feel free to contact me ([xxszqyy@gmail.com](xxszqyy@gmail.com)).

## Acknowledgements
* [TorchSemiSeg](https://github.com/charlesCXK/TorchSemiSeg) 
* [UA-MT](https://github.com/yulequan/UA-MT)
* [SSL4MIS](https://github.com/HiLab-git/SSL4MIS)