Topological-DVAE
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An implementation of Denoising Variational AutoEncoder with Topological loss
Topological-DVAE
An implementation of Denoising Variational AutoEncoder with Topological loss
Requirements
1. PyTorch
2. SimpleITK
3. tqdm
4. visdom
5. TopologyLayer
6. Gudhi
Usage
Training Model: main.py
Evaluation: eval.py
Check PH: PH.py
Reference
[1] Kingma, D. P., & Welling, M. (2013). "Auto-Encoding Variational Bayes", (Ml), 1–14. https://doi.org/10.1051/0004-6361/201527329
[2] James R. Clough, et al. (2019). "A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology". https://arxiv.org/abs/1910.01877
[3] https://github.com/pytorch/examples/tree/master/vae
[4] https://github.com/JamesClough/topograd
[5] https://github.com/bruel-gabrielsson/TopologyLayer