Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study

Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study

Advancements in machine learning hold great promise for the analysis of multimodal neuroimaging data. They can help identify biomarkers and improve diagnosis for various neurological disorders. However, the application of such techniques for rare and heterogeneous diseases remains challenging due to...

Full description

Saved in:
Bibliographic Details
Main Authors: Shailesh Appukuttan, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Neuroinformatics
Subjects:
amyotrophic lateral sclerosis
machine learning
multimodal MRI
small cohort
classification
pipeline optimization
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2025.1568116/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/fninf.2025.1568116/full

Similar Items