oliverchampion/DCENET

Neural networks for fitting DCE data

DCE-NET

Framework for estimating DCE-MRI physiological parameters.

Getting Started:

To create the environment dce in anaconda, the following command can be used:

conda env create -f environment.yml

Simulations:

Executing the framework on simulations can be done using main.py.

usage: main.py [--nn] [--layers] [--lr] [--batch_size] [--attention]
               [--bidirectional] [--supervised] [--results]

optional arguments:
  --nn            neural network to use - linear / lstm / gru
  --layers        number and size of layers - linear:   neurons_layer_1 neurons_layer_2 ...
                                            - lstm/gru: hidden_dimension stacked_layers
  --lr            learning rate - float
  --batch_size    batch size - int
  --attention     option to include attention layers for lstm/gru
  --bidirectional option to include bidirectionality for lstm/gru
  --supervised    option to train on ground truth parameters
  --results       option to perform evaluation on network using different SNR (must be trained first)

More hyperparameters are stored in hyperparameters.py.

Showing Results

Results of the trained frameworks can be obtained by executing results.py.