Care For Your Ovary

A computer-aided diagnoisis system for ovarian tumor based on ultrasonic images

Main Function:

Load ultrasonic Image (Support .PNG / .JPG)
Classification of ovarian lesions (Current models include ResNet, ResNeXt, and DenseNet)
Visualization of the classification result
Segmentation of ovarian lesions（Current models include UNet，DeepLabv3plus, and PSPNet)
Save the segmentation result, segmentation mask, and visualization result

Display

Ovarian Lesion Segmentation

Visualization of Lesion Classification

Environment Setup

Clone the repo: git clone https://github.com/1024803482/CareForYourOvary

Setup environment:

# Download libs
pip install numpy 
pip install PyQT5 
pip install torch==1.8.0 torchvision==0.9.0
pip install matplotlib
pip install einops
pip install segmentation_models_pytorch==0.2.1
pip install opencv
pip install imageio==2.9.0
pip install PIL

Weights

The weights of classifier and segmenter can be download:

BaiDuYun: https://pan.baidu.com/s/1ZzAd3mvGeFx-2dJlWEDpqw , password: rc72
Google Drive: something error.

The .EXE can be run directly:

BaiDuYun: https://pan.baidu.com/s/17OQu5WpjSRa3bkVSO4ei1Q , password: n2o0
Google Drive: something error.

Note

This system is used for academic purposes, please indicate the source. We will update our system soon!

If you have any question, please discuss with me by sending email to cailh@buaa.edu.cn / ceilinghans@gmail.com.

Citation

if you find this code helpful, please cite:

@article{DBLP:journals/corr/abs-2207-06799,
  author    = {Qi Zhao and
               Shuchang Lyu and
               Wenpei Bai and
               Linghan Cai and
               Binghao Liu and
               Meijing Wu and
               Xiubo Sang and
               Min Yang and
               Lijiang Chen},
  title     = {A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation},
  journal   = {CoRR},
  volume    = {abs/2207.06799},
  year      = {2022},
}

@inproceedings{cai2022using,
  author = {Cai Linghan and
            Wu Meijing and 
            Chen Lijiang and
            Bai Wenpei and
            Yang Min and
            Lyu Shuchang and
            Zhao, Qi},
  title =  {Using Guided Self-Attention with Local Information for Polyp Segmentation},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages = {629--638},
  year = {2022},
  organization={Springer}
}

1024803482/CareForYourOvary