zmbhou / UA-MT

code for MICCAI 2019 paper 'Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation'.

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

by Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng.

Introduction

This repository is for our MICCAI 2019 paper 'Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation'.

Installation

This repository is based on PyTorch 0.4.1.

Usage

1. Clone the repository:

   git clone https://github.com/yulequan/UA-MT.git
   cd UA-MT

2. Put the data in data/2018LA_Seg_TrainingSet.

3. Train the model:

   cd code
   python train_LA_meanteacher_certainty_unlabel.py --gpu 0

Citation

If UA-MT is useful for your research, please consider citing:

@inproceedings{yu2018pu,
     title={Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation},
     author={Yu, Lequan and Wang, Shujun and Li, Xiaomeng and Fu, Chi-Wing and Heng, Pheng-Ann},
     booktitle = {MICCAI},
     year = {2019} }

Questions

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