Visit our hands-on tutorial web site to try ClinicaDL directly in a Google Colab instance!
clinicadl can be installed on Mac OS X and Linux machines, and possibly on Windows computers with a Linux Virtual Machine.
We assume that users installing and using
clinicadl are comfortable with using the command line.
User documentation (
Prepare your imaging data¶
clinicadl preprocessing run- Preprocessing pipelines
clinicadl preprocessing quality-check- Quality control of preprocessed data: use a pretrained network [Fonov et al., 2018] to classify adequately registered images.
clinicadl preprocessing extract-tensor- Prepare input data for deep learning with PyTorch
Train & test your classifier¶
clinicadl random-search- Explore hyperparameters space by training random models
clinicadl train- Train with your data and create a model
clinicadl classify- Classify one image or a list of images with your previously trained CNN
clinicadl interpret- Interpret trained CNNs on individual or group of images
clinicadl generate- Generate synthetic data for functional tests
clinicadl tsvtool- Handle TSV files for metadata processing and data splits
Pretrained models for the CNN networks implemented in ClinicaDL can be obtained here: https://zenodo.org/record/3491003
These models were obtained during the experiments for publication. They correspond to a previous version of ClinicaDL, hence their file system is not compatible with the current version. Updated versions of most representative models are available here.
clinicadl is distributed under the terms of the MIT license given here.
For publications or communications using
clinicadl, please cite [Wen et al., 2020]
as well as the references mentioned on the wiki page of the pipelines you used
(for example, citing PyTorch when using the
clinicadl is a software for research studies. It is not intended for use in medical routine.