`train` - Custom experiments¶

The aim of clinicadl is not only to provide a collection of tools, but also to allow users to add their own in the framework. Before starting, please fork and clone the github repo.

Tip

Do not hesitate to ask for help on GitHub or propose a new pull request!

Custom architecture¶

Custom CNN architectures can be added to clinicadl by adding a model class in clinicadl/tools/deep_learning/models and importing it in clinicadl/tools/deep_learning/models/__init__.py.

There are two rules to follow to convert this CNN architecture into an autoencoder:

Implement the convolutional part in features and the fully-connected layer in classifier. See predefined models as examples.
Check that all the modules used in your architecture are in the list of modules transposed by the autoencoder or that the invert version of this module is itself (it is the case for activation layers).

Custom input type¶

Input types that are already provided in clinicadl are image, patch, roi and slice. To add a custom input type, please follow the steps detailed below:

Choose a mode name for this input type (for example default ones are image, patch, roi and slice).
Add your dataset class in clinicadl/tools/deep_learning/data.py as a child class of the abstract class MRIDataset.

Create your dataset in return_dataset by adding:

elif mode==<mode_name>:
    return <dataset_class>(
        input_dir,
        data_df,
        preprocessing=preprocessing,
        transformations=transformations,
        <custom_args>
    )

Add your custom subparser to train and complete train_func in clinicadl/cli.py.

Custom preprocessing¶

Define the path of your new preprocessing in the _get_path method of MRIDataset in clinicadl/tools/deep_learning/data.py.

You will also have to add the name of your preprocessing pipeline in the general command line by modifying the possible choices of the preprocessing argument of train_pos_group in cli.py.

Custom labels¶

You can launch a classification task with clinicadl using any label. The input tsv files must include the columns participant_id, session_id and diagnosis.

If the column diagnosis does not contain the labels described in the dedicated section (AD, CN, MCI, sMCI or pMCI), you can add your own label name associated to a class value in the diagnosis_code of the class MRIDataset in clinicadl/tools/deep_learning/data.py.

train - Custom experiments¶