# transfer-contrastive-learning

**Repository Path**: libertyme/transfer-contrastive-learning

## Basic Information

- **Project Name**: transfer-contrastive-learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-03
- **Last Updated**: 2025-03-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TCLP
Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification


# Dataset

### Source data
This dataset was originally collected by Erzina et al.(2020). I have added the dataset to this project while you can also download it from Kaggle, [Cells Raman Spectra](https://www.kaggle.com/datasets/andriitrelin/cells-raman-spectra)


### Target data
Currently, a random dataset was generated for running the baseline code, a simulated dataset generated by GNN which also follows the original distribution will be released after paper has been accepted.
# Run

### ML baseline
```
cd src
python ml_baseline.py
```
This command runs the 6 traditional machine learning models: LogisticRegression, SVC, RandomForestClassifier, DecisionTreeClassifier, KNeighborsClassifier

### NN baseline
```
cd src
python nn_baseline.py
```

This commands runs the 4 neural network models, including 1 MLP, 1 LSTM, and 2 CNNs

### TCLP
```
cd src
python train_models.py
```

# Cite
If you find this is useful, please cite our paper
```
@article{wang2024transfer,
  title={Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification},
  author={Wang, Zhiqiang and Lin, Yanbin and Zhu, Xingquan},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2024},
  publisher={IEEE}
}
```