Models¶
U-net¶
synthtorch.models.unet
holds the architecture for a 2d or 3d unet [1,2,3]
References
- [1] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox.
- “U-net: Convolutional networks for biomedical image segmentation.” International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
- [2] O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger,
- “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2016, pp. 424–432.
- [3] C. Zhao, A. Carass, J. Lee, Y. He, and J. L. Prince, “Whole Brain Segmentation and Labeling
- from CT Using Synthetic MR Images,” MLMI, vol. 10541, pp. 291–298, 2017.
Author: Jacob Reinhold (jacob.reinhold@jhu.edu)
Created on: Nov 2, 2018
Variational Autoencoder¶
synthtorch.models.vae
construct a variational autoencoder
Author: Jacob Reinhold (jacob.reinhold@jhu.edu)
Created on: Jan 29, 2019
N-layer CNN¶
synthtorch.models.nconvnet
define the class for a N layer CNN with no max pool, increase in channels, or any of that fancy stuff. This is generally used for testing purposes
Author: Jacob Reinhold (jacob.reinhold@jhu.edu)
Created on: Nov 2, 2018
DenseNet¶
synthtorch.models.densenet
holds the architecture for a 2d densenet [1] this model is pulled (and modified) from the pytorch repo: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
References
- [1] Huang, Gao, et al. “Densely connected convolutional networks.”
- Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
Author: Jacob Reinhold (jacob.reinhold@jhu.edu)
Created on: Apr 8, 2018