MTGS:Multi-task learning for concurrent grading diagnosis and semi-supervised segmentation of honeycomb lung in CT images

by Yunyun Dong,Bingqian Yang,Xiufang Feng

Introduction

This repository is the Pytorch implementation of "Multi-task learning for concurrent grading diagnosis and semi-supervised segmentation of honeycomb lung in CT images"

Requirements

We implemented our experiment on the computer system of Taiyuan University of Technology. The specific configuration is as follows:

• Centos 7.4
• RTX Nvidia 3090 24G

Some important required packages include:

• CUDA 11.6
• Pytorch == 1.12.0
• Python == 3.9
• Some basic python packages such as Numpy, Scikit-image, Scipy ......

Usage

1. Download the Kvasir-SEG and Honeycomb dataset in Google drive. Put the data in './data/' folder
2. Train the model

cd code
python train_mynetwork.py

3. Test the model

cd code
python test_mynetwork.py

Acknowledgement

Part of the code is revised from the CTCT.

We thank Dr. Xiangde Luo for their elegant and efficient code base.

Note

• The repository is being updated.
• Contact: Xiufang Feng (fxf_tyut@163.com)