GithubHelp home page GithubHelp logo

tclan8023 / ss-net Goto Github PK

View Code? Open in Web Editor NEW

This project forked from ycwu1997/ss-net

0.0 0.0 0.0 79.71 MB

Official Code for our MICCAI 2022 paper "Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation"

License: MIT License

Shell 0.48% Python 99.52%

ss-net's Introduction

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

by Yicheng Wu*, Zhonghua Wu, Qianyi Wu, Zongyuan Ge, and Jianfei Cai.

News

<12.06.2022> We provided our pre-trained models on the LA and ACDC datasets, see './SS-Net/pretrained_pth';
<09.06.2022> We released the codes;

Introduction

This repository is for our MICCAI 2022 paper: 'Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation'.

Requirements

This repository is based on PyTorch 1.8.0, CUDA 11.1 and Python 3.8.10. All experiments in our paper were conducted on a single NVIDIA Tesla V100 GPU with an identical experimental setting.

Usage

  1. Clone the repo.;
git clone https://github.com/ycwu1997/SS-Net.git
  1. Put the data in './SS-Net/data';

  2. Train the model;

cd SS-Net
# e.g., for 5% labels on LA
python ./code/train_ss_3d.py --labelnum 4 --gpu 0
  1. Test the model;
cd SS-Net
# e.g., for 5% labels on LA
python ./code/test_LA.py --labelnum 4

Citation

If our SS-Net model is useful for your research, please consider citing:

  @inproceedings{wu2022exploring,
    title={Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation},
    author={Wu, Yicheng and Wu, Zhonghua and Wu, Qianyi and Ge, Zongyuan and Cai, Jianfei},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={34--43},
    volume={13435},
    year={2022},    
    doi={10.1007/978-3-031-16443-9\_4},
    organization={Springer, Cham}
    }

Acknowledgements:

Our code is adapted from MC-Net, SemiSeg-Contrastive, VAT, and SSL4MIS. Thanks for these authors for their valuable works and hope our model can promote the relevant research as well.

Questions

If any questions, feel free to contact me at '[email protected]'

ss-net's People

Contributors

ycwu1997 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.