Awesome
SLC-Net
This repository is for our paper "Semi-supervised medical image segmentation using cross-style consistency with shape-aware and local context constraints"
Requirements
Some important required packages include:
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Pytorch version >=0.4.1.
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TensorBoardX
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Python == 3.7
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Efficientnet-Pytorch
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Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy,Batchgenerators ......
Usage
1、Clone the repo;
git clone https://github.com/igip-liu/SLC-Net.git
2、Data Preparation;
The division method of training/validation/test set can be seen:
The data that can be used to train our code can be seen:
The division of labeled/unlabeled datasets can be found in this code
You can regenerate the training data:
cd SLC-Net/code/dataloaders
python acdc_data_processing.py
3、Train the model;
cd SLC-Net/code
CUDA_VISIBLE_DEVICES=3 python train_CLB.py --root_path ../data/ACDC --exp ACDC/SLC-Net --num_classes 4 --labeled_num 7 --use_block_dice_loss --block_num 4
4、Test the model;
cd SLC-Net/code
CUDA_VISIBLE_DEVICES=0 python test_2D_fully.py --root_path ../data/ACDC --exp ACDC/SLC-Net --num_classes 4 --labeled_num 7
Our code is based on the UAMT, SSL4MIS and Dual-Normalization. Thanks for these authors for their valuable works.