Awesome

Dual-task Consistency

Code for this paper: Semi-supervised Medical Image Segmentation through Dual-task Consistency (AAAI2021)

@inproceedings{luo2021semi,
  title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
  author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={10},
  pages={8801--8809},
  year={2021}
}

Requirements

Some important required packages include:

Pytorch version >=0.4.1.
TensorBoardX
Python == 3.6
Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy ......

Follow official guidance to install Pytorch.

Usage

Clone the repo:

git clone https://github.com/HiLab-git/DTC.git 
cd DTC

Put the data in data/2018LA_Seg_Training Set.
Train the model

cd code
python train_la_dtc.py

Test the model

python test_LA.py

Our pre-trained models are saved in the model dir DTC_model (both 8 labeled images and 16 labeled images), and the pretrained SASSNet and UAMT model can be download from SASSNet_model and UA-MT_model. The other comparison method can be found in SSL4MIS

Results on the Left Atrium dataset (SOTA).

The training set consists of 16 labeled scans and 64 unlabeled scans and the testing set includes 20 scans.

Methods	DICE (%)	Jaccard (%)	ASD (voxel)	95HD (voxel)	Reference	Released Date
UAMT	88.88	80.21	2.26	7.32	MICCAI2019	2019-10
SASSNet	89.54	81.24	2.20	8.24	MICCAI2020	2020-07
DTC	89.42	80.98	2.10	7.32	AAAI2021	2020-09
LG-ER-MT	89.62	81.31	2.06	7.16	MICCAI2020	2020-10
DUWM	89.65	81.35	2.03	7.04	MICCAI2020	2020-10
MC-Net	90.34	82.48	1.77	6.00	Arxiv	2021-03

The training set consists of 8 labeled scans and 72 unlabeled scans and the testing set includes 20 scans.

Methods	DICE (%)	Jaccard (%)	ASD (voxel)	95HD (voxel)	Reference	Released Date
UAMT	84.25	73.48	3.36	13.84	MICCAI2019	2019-10
SASSNet	87.32	77.72	2.55	9.62	MICCAI2020	2020-07
DTC*	87.51	78.17	2.36	8.23	AAAI2021	2020-09
LG-ER-MT	85.54	75.12	3.77	13.29	MICCAI2020	2020-10
DUWM	85.91	75.75	3.31	12.67	MICCAI2020	2020-10
MC-Net	87.71	78.31	2.18	9.36	Arxiv	2021-03

Note that, * denotes the results from MC-Net and the model has been openly available (provided by Dr. YiCheng), thanks for Dr. Yicheng.

Acknowledgement

This code is adapted from UA-MT, SASSNet, SegWithDistMap.
We thank Dr. Lequan Yu, M.S. Shuailin Li and Dr. Jun Ma for their elegant and efficient code base.
More semi-supervised learning approaches for medical image segmentation have been summarized in SSL4MIS.