Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. - Mathilde Giacalone; Pejman Rasti; Noëlie Debs; Carole Frindel; Tae-Hee Cho; Emmanuel Grenier; David Rousseau (Medical Image Anal., 2018)

Evaluation of the Realism of an MRI Simulator for Stroke Lesion Prediction Using Convolutional Neural Network. - Noëlie Debs; Méghane Decroocq; Tae-Hee Cho; David Rousseau; Carole Frindel (SASHIMI@MICCAI, 2019)

Motion sensor data anonymization by time-frequency filtering. - Noëlie Debs; Théo Jourdan; Ali Moukadem; Antoine Boutet; Carole Frindel (EUSIPCO, 2020)

Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. - Noëlie Debs; Pejman Rasti; Léon Victor; Tae-Hee Cho; Carole Frindel; David Rousseau (Comput. Biol. Medicine, 2020)

Prédiction de l'issue lésionnelle et clinique de l'accident vasculaire cérébral par des approches d'apprentissage automatique. (Prediction of lesion and clinical outcome of stroke using an artificial intelligence approach). - Noëlie Debs (2020)

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. - Théo Jourdan; Noëlie Debs; Carole Frindel (Sensors, 2021)

Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection. - Noëlie Debs; Alexandre Routier; Clément Abi Nader; Arnaud Marcoux; Alexandre Bône; Marc-Michel Rohé (AMAI@MICCAI, 2023)

Physics-Informed Autoencoder for DSC-MRI Perfusion Post-Processing: Application to Glioma Grading. - Pierre Fayolle; Alexandre Bône; Noëlie Debs; Mathieu Naudin; Pascal Bourdon; Rémy Guillevin; David Helbert (ISBI, 2025)