3 papers
cs.CV2023
A Novel Autoencoders-LSTM Model for Stroke Outcome Prediction using Multimodal MRI Data
Nima Hatami, Laura Mechtouff, David Rousseau +4
Patient outcome prediction is critical in management of ischemic stroke. In this paper, a novel machine learning model is proposed for stroke outcome prediction using multimodal Ma…
eess.IV2022
CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
Nima Hatami, Tae-Hee Cho, Laura Mechtouff +3
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous da…
cs.CG2022
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
Méghane Decroocq, Carole Frindel, Pierre Rougé +2
Computational fluid dynamics (CFD) simulation provides valuable information on blood flow from the vascular geometry. However, it requires extracting precise models of arteries fro…