Code for the paper Factorised spatial representation learning: application in semi-supervised myocardial segmentation.
The main files are:
- sdnet.py: the model implementation
- sdnet_trainer.py: code related to running an experiment
Data loaders are stored in the loaders package. To define a new loader, extend class base_loader.Loader, and add initialisation in loader_factory.py. The data folder location can be specified in parameters.py.
The main method is in main.py and arguments can be passed at runtime. For example, an experiment can be run with:
python main.py --dataset acdc --split 0 --ul_mix 1 --l_mix 0.5
--split defines the cross validation data split, --ul_mix the percentage of unlabelled data, and --l_mix the percentage of labelled images. These proportions are calculated by comparing with the total number of labelled images in the dataset.
If you use this code for your research, please cite our paper:
@InProceedings{chartsias2018factorised,
author="Chartsias, Agisilaos
and Joyce, Thomas
and Papanastasiou, Giorgos
and Semple, Scott
and Williams, Michelle
and Newby, David
and Dharmakumar, Rohan
and Tsaftaris, Sotirios A.",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="Factorised Spatial Representation Learning: Application in Semi-supervised Myocardial Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="490--498",
isbn="978-3-030-00934-2"
}