Nidl¶
Nidl is a Python library to perform distributed training and evaluation of deep learning models on large-scale neuroimaging data (anatomical volumes and surfaces, fMRI).
It follows the PyTorch design for the training logic and the scikit-learn API for the models (in particular fit, predict and transform).
Supervised, self-supervised and unsupervised models are available (with pre-trained weights) along with open datasets.
Get started with Nidl
Discover functionalities by reading examples
Learn about neuroimaging analysis
Featured works¶
OpenBHB dataset
B Dufumier et al.: OpenBHB - a Large-Scale Multi-Site Brain MRI Dataset for Age Prediction and Debiasing, NeuroImage 2022.
SimCLR
T Chen et al.: A Simple Framework for Contrastive Learning of Visual Representations, ICML 2020.
Barlow Twins
Zbonta et al.: Barlow Twins, Self-Supervised Learning via Redundancy Reduction, PMLR 2021.
y-Aware weakly supervised learning
B Dufumier et al.: Exploring the potential of representation and transfer learning for anatomical neuroimaging - Application to psychiatry, NeuroImage 2024.