junqiangchen / TN-SCUI2020-Challenge

TN-SCUI2020-Challenge

TN-SCUI2020-Challenge

This is an example of the Us imaging is used to segment and classify thyroid nodule.

Prerequisities

The following dependencies are needed:

• numpy >= 1.11.1
• SimpleITK >=1.0.1
• tensorflow-gpu ==1.14.0
• pandas >=0.20.1
• scikit-learn >= 0.17.1

How to Use

• 1、when download the all project,check out the segmeatationdata.csv and classifydata.csv,put your train data into same folder.
• 2、run vnet2d_train.py for vnet2d segmeatation training:make sure train data have effective path
• 3、run vnet2d_inference.py for vnet2d segmeatation inference:make sure test data have effective path
• 4、run resnet2d_train.py for resnet2d classify training:make sure train data have effective path
• 5、run resnet2d_inference.py for resnet2d classify inference:make sure test data have effective path

Result

• segment train loss and train accuracy

• classify train loss and train accuracy

• test dataset segmentation result：left is source image,median is ground truth mask,right is predict mask

• test dataset leadboard

• more detail and trained model can follow my WeChat Public article.

Contact

• https://github.com/junqiangchen
• email: [e-mail]
• Contact: junqiangChen
• WeChat Number: 1207173174
• WeChat Public number: 最新医学影像技术