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predict - Inference using pretrained models

This functionality performs individual prediction and metrics computation on a set of data using models trained with clinicadl train or clinicadl random-search generate tasks. It can also use any pretrained models if they are structured like a MAPS.

unbiased image-level results

For patch, roi and slice models, the predictions of the models on the validation set are needed to perform unbiased ensemble predictions at the image level. If the tsv files in fold-<fold>/best-<metric>/validation were erased the task cannot be run.


Please check which preprocessing needs to be performed in the maps.json file in the results folder. If it has not been performed, execute the preprocessing pipeline as well as clinicadl extract to obtain the tensor versions of the images.

Running the task

This task can be run with the following command line:


  • INPUT_MAPS_DIRECTORY (Path) is the path to the MAPS of the pretrained model.
  • DATA_GROUP (str) is the name of the data group used for the prediction.

data group consistency

For ClinicaDL, a data group is linked to a list of participants / sessions and a CAPS directory. When performing a prediction, interpretation or tensor serialization the user must give a data group. If this data group does not exist, the user MUST give a caps_directory and a participants_tsv. If this data group already exists, the user MUST not give any caps_directory or participants_tsv, or set overwrite to True.

Optional arguments:

  • Computational resources
    • --gpu / --no-gpu (bool) Uses GPU acceleration or not. Default behaviour is to try to use a GPU. If not available an error is raised. Use the option --no-gpu if running in CPU.
    • --n_proc (int) is the number of workers used by the DataLoader. Default: 2.
    • --batch_size (int) is the size of the batch used in the DataLoader. Default: 2.
  • Other options
    • --caps_directory (Path) is the input folder containing the neuroimaging data (tensor version of images, output of clinicadl extract pipeline) in a CAPS hierarchy.
    • --participants_tsv (Path) is a path to a TSV file with subjects/sessions to process (filename included), OR the path to the test folder of a split directory obtained with clinicadl tsvtool split.
    • --labels/--no_labels (bool) is a flag to add if the dataset does not contain ground truth labels. Default behaviour will look for ground truth labels and raise an error if not found.
    • --selection_metrics (List[str]) is a list of metrics to find the best models to evaluate. Default will predict the results for best model based on the loss only.
    • --diagnoses (List[str]) if tsv_file is a split directory, then will only load the labels wanted. Default will look for the same labels used during the training task.
    • --multi_cohort (bool) is a flag indicated that multi-cohort classification is performed. In this case, caps_directory and tsv_path must be paths to TSV files.
    • --overwrite (bool) is a flag allowing to overwrite a data group to redefine it. All results obtained for this data group will be erased.


Results are stored in the MAPS of path model_path, according to the following file system:

    ├── fold-0  
    ├── ...  
    └── fold-<i>
        └── best-<metric>
                └── <data_group>
                    ├── description.log
                    ├── <prefix>_image_level_metrics.tsv
                    ├── <prefix>_image_level_prediction.tsv
                    ├── <prefix>_{patch|roi|slice}_level_metrics.tsv
                    └── <prefix>_{patch|roi|slice}_level_prediction.tsv
The last two TSV files will be absent if the model takes as input the whole image. Moreover, *_metrics.tsv files are not computed if --no_labels is given. The content of *_prediction.tsv files depend on the task performed during the training task.