Use of deep learning in the MRI diagnosis of Chiari malformation type I

PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making. METHODS: A retrospective MRI dataset of patients diagnosed with CM1 and healthy indivi...

Descripción completa

Detalles Bibliográficos
Autores principales: Tanaka, Kaishin W., Russo, Carlo, Liu, Sidong, Stoodley, Marcus A., Di Ieva, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Diagnostic Neuroradiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271110/
https://www.ncbi.nlm.nih.gov/pubmed/35199210
http://dx.doi.org/10.1007/s00234-022-02921-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271110/
https://www.ncbi.nlm.nih.gov/pubmed/35199210
http://dx.doi.org/10.1007/s00234-022-02921-0

Ejemplares similares