`clinicadl classify` - Inference using pretrained models¶

This functionality performs image classification using models trained with clinicadl train task. It can also use pretrained models if their folder structure is similar to the structure created by the command clinicadl train. At the top level of each model folder there are two files:

environment.txt describes the Python and Pytorch version used during the training.
commandline.json describes the training parameters used to create the model.

This two files allow clinicadl to load the model(s) that led to the best validation balanced accuracy (.pth.tar file).

Warning

For patch, roi and slice models, the predictions of the models on the validation set are needed to perform unbiased soft-voting and find the prediction on the image level. If the tsv files in cnn_classification/best_balanced_accuracy were erased the task cannot be run.

Prerequisites¶

In order to execute this task, the input images must be listed in a tsv_file formatted using the CAPS definition. Please check which preprocessing needs to be performed in the commandline.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.

Some pretrained models are available to download here. You can download them using your navigator or the command line. For example, to get the model "Image-based" with a single split type:

curl -k https://aramislab.paris.inria.fr/files/data/models/dl/models_v002/model_exp3_splits_1.tar.gz  -o model_exp3_splits_1.tar.gz
tar xf model_exp3_splits_1.tar.gz

Running the task¶

This task can be run with the following command line:

clinicadl classify <caps_directory> <tsv_file> <model_path>

where:

caps_directory (str) is the input folder containing the neuroimaging data (tensor version of images, output of clinicadl extract pipeline) in a CAPS hierarchy.
tsv_file (str) is a TSV file with subjects/sessions to process (filename included).
model_path (str) is a path to the folder where the model and the json file are stored.

Optional arguments:

--prefix_output (str) is a prefix in the output filenames. Default value: prefix_DB.
--use_extracted_features (bool) is a flag to use extracted slices or patches, if not specified they will be extracted on the fly from the complete image (if necessary). Default value: False.
--use_cpu (bool) forces to use CPU. Default behaviour is to try to use a GPU and to raise an error if it is not found.

Outputs¶

Results are stored in the results folder given by model_path, according to the following file system:

<model_path>
    ├── fold-0  
    ├── ...  
    └── fold-i  
        └── cnn_classification
                └── best_balanced_accuracy
                    ├── <prefix_output>_image_level_metrics.tsv
                    ├── <prefix_output>_image_level_prediction.tsv
                    ├── <prefix_output>_{patch|roi|slice}_level_metrics.tsv
                    └── <prefix_output>_{patch|roi|slice}_level_prediction.tsv

The last two TSV files will be absent if the model takes as input the whole image.

clinicadl classify - Inference using pretrained models¶

Prerequisites¶

Running the task¶

Outputs¶

`clinicadl classify` - Inference using pretrained models¶