Paper accepted for publication at MELBA: Perkonigg, M., Hofmanninger, J., Herold, C., Prosch, H., & Langs, G. (2021). Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics. https://arxiv.org/abs/2111.13069
1. Requirements
All experiments are performed with PyTorch 1.7.1 using Python 3.6. The requirements are given in requirements.txt.
Estimated install time on a PC: 30-60 minutes.
Original experiments were performed on NVIDIA GPUs with Linux CentOS 7.
2. Data set
The data set used can be downloaded from the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms), https://www.ub.edu/mnms/.
After downloading it is necessary to preprocess the data by running:
python data_prep/data_prep_cardiac.py <download_path> <dataset_path>
where <download_path> is the directory where the M&Ms dataset was downloaded to, and <dataset_path> is where the preprocessed data is stored.
3. Training
To run the training the config files in training_configs/ are used. Please modify output and input directories as needed.
python run_training.py --config training_configs/cardiac_base.yml
python run_training.py --config training_configs/cardiac_casa.yml
4. Results
For convenience examples of a full analysis for cardiac segmentation are given in evaluation/cardiac_evaluation.ipynb.