NPC-diagnosis-based-on-deep-learnig

This is a PyTorch implementation of the paper 'Computer Aided Pathological Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning', and We'll refine the data and code over time.

🤝 Authorization

If you would like to use our data, please contact us first and obtain authorization to use it.

🤝 Citation

If you find this code is useful for your research, please consider citing:

@article{
title={Computer Aided Pathological Diagnosis of Nasopharyngeal Carcinoma Based on  Deep Learning}，
author={Songhui Diao, Jiaxin Hou, Hong Yu, Xia Zhao, Yikang Sun, Ricardo Lewis Lambo, Yaoqin Xie, Lei Liu, Weiren Luo, Wenjian Qin}，
journal={The American Journal of Pathology}，
year={2020},
}

Setup

Prerequisites

• PyTorch 1.0
• python 3.6.4
• Torchvision 0.4
• numpy and so on

Data

• train data
• validation data
• test data

Training

Training a network with default arguments. Model checkpoints and tensorboard logs are written out to a unique directory created by default within experiments/models and experiments/logs respectively after starting training. If conditions permit, it will be better to pre-train the model first.

python main.py

Validation and Testing

You can run validation and testing on the checkpointed best model by:

python test.py