UNet for Loow Dose CT
This repository contains my implementation of a U-Net similar to the FBPConvNet after:
K. H. Jin, M. T. McCann, E. Froustey and M. Unser, "Deep Convolutional Neural Network for Inverse Problems in Imaging," in IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4509-4522, Sept. 2017, https://doi.org/10.1109/TIP.2017.2713099 https://github.com/panakino/FBPConvNet
Project structure
Unet
├───mlruns
│ '''trained models and losses'''
├───models
│ '''trained models'''
│ └───losses
│ '''loss files of the trained models'''
├───msub
│ '''scripts for high performance cluster job management'''
│
│ conda_env_nn.yml
│ '''conda environment file (prerequisites)'''
│ data.py
│ '''functions for data loading and preparation'''
│ data_inspect.py
│ '''script to inspect single data samples (FBP and ground truth)'''
│ eval.py
│ '''script to evaluate data with a trained model'''
│ losses.py
│ '''script to inspect and plot losses of a trained model'''
│ model.py
│ '''UNet class and functions'''
│ orig_model.py
│ '''FBPConvNet class and functions'''
│ README.md
│ '''information on the repository'''
│ train.ipynb
│ '''jupyter notebook to train a model on the high performance cluster'''
│ train.py
│ '''script to train a model on the high performance cluster'''
│ trainer.py
│ '''Trainer class and functions'''
│ train_locally.py
│ '''script to train a model on my local machine'''
│ train_test.py
│ '''script to train a model with small datasets'''
│ utils.py
│ '''additional functions'''