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APD-Net with Pathology

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 The datapath is specified in

To run an experiment, execute, 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 --config myconfiguration --split 0 --l_mix 1-1

To run an test, execute as follows:

python --config myconfiguration --split 0 --l_mix 1-1 --test True


If you use this code for your research, please cite our paper:

  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},


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