Code for reproducing the key results of our IPMI 2017 paper on structured sparsity penalization for large functional neuroimaging datasets.
Bzdok D, Eickenberg M, Varoquaux G, Thirion B. Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging Information Processing in Medical Imaging (IPMI 2017). Paper on ResearchGate
Please cite this paper when using this code for your research.
To follow established conventions of scikit-learn estimators, the StructuredEstimator class exposes the functions fit(), predict(), and score(). This should allow for seamless integration into other scikit-learn-enabled machine-learning pipelines.
For questions and bug reports, please send me an e-mail at danilobzdok[at]gmail.com.
- Make sure that recent versions of the following packages are available:
- Python (version 2.7 or higher)
- Numpy (e.g.
pip install numpy) - Nibabel (e.g.,
pip install nibabel)
- Nilearn (e.g.,
pip install nilearn) - Scikit-learn (e.g.,
pip install scikit-learn) - SPAMS (see http://spams-devel.gforge.inria.fr/downloads.html)
- Clone this repository, e.g.:
git clone https://github.com/banilo/ipmi2017.git