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Explainable Shape Analysis through Deep Generative Models DOI

The Tensorflow code in this repository implements the modifications of the VAE and Ladder VAE frameworks presented in Learning Interpretable Anatomical Features through Deep Generative Models: Application to Cardiac Remodeling and Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodelling papers.

Usage

Make sure you have Python 3.4 and Tensorflow installed.

The architecture and training details can be configured in the config/config.json file.

To train the network please run:

python training.py --config=jsons/config.json


Acknowledgments

This implementation was inspired by geosada Tensorflow implementation of the LVAE original paper. If you find this work useful, please cite the following papers:

[1] Biffi, C., et al. Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodelling Submitted for review to IEEE Transactions on Medical Imaging.

[2] Biffi, C., et al. Learning Interpretable Anatomical Features through Deep Generative Models: Application to Cardiac Remodeling International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018.

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