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- Reproducibility in machine learning for medical imaging
- Interpretability of Machine Learning Methods Applied to Neuroimaging
- Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET
- How can data augmentation improve attribution maps for disease subtype explainability?
- Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data
- Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practice
- Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
- Advances in the Clinica software platform for clinical neuroimaging studies
- ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing