Implementation of the APDNet model to perform disentanglement of anatomical, modality, and pathology information in medical images. For further details please see our [paper], accepted in [MICCAI-2020 Workshop: DART].
Python dependencies to run the code is listed in the file ;requirements.txt'.
The structure of this project is the following:
- configuration: package containing configuration parameters for running an experiment.
- layers: package with custom Keras layers
- loaders: package with data loaders
- models: package with the SDNet model and other Keras models
- model_executors: package with scripts for running an experiment
- callbacks: package with Keras callbacks for printing images and losses during training
- DataProcess: package with some of the data preprocess codes for some public datasets
To define a new data loader, extend class base_loader.Loader, and register the loader in loader_factory.py. The datapath is specified in parameters.py.
To run an experiment, execute experiment.py, passing the configuration filename, the split number as runtime parameters, and the pathology annotation amount 'l-mix a-b', where 'a' denotes the amount of volumes among the full training data, while 'b' represents the amount of data in each volume:
python experiment.py --config myconfiguration --split 0 --l_mix 1-1
To run an test, execute experiment.py as follows:
python experiment.py --config myconfiguration --split 0 --l_mix 1-1 --test True
Citation
If you use this code for your research, please cite our paper:
@incollection{jiang2020pathology,
title={Semi-supervised Pathology Segmentation with Disentangled Representations},
author={Haochuan, Jiang and Chartsias, Agisilaos and Papanastasiou, Giorgos and Semple, Scott and Dweck, Mark and and Dharmakumar, Rohan and Tsaftaris, Sotirios A},
booktitle={Domain Adaptation and Representation Transfer},
year={2020},
publisher={Springer}
}