DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults | Zendy

Hailong Li | Zendy; Lili He | Zendy; Jonathan A. Dudley | Zendy; Thomas Maloney | Zendy; Elanchezhian Somasundaram | Zendy; Samuel L. Brady | Zendy; Nehal A. Parikh | Zendy; Jonathan R. Dillman | Zendy

Open Access

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults

Author(s) -

Hailong Li,

Lili He,

Jonathan A. Dudley,

Thomas Maloney,

Elanchezhian Somasundaram,

Samuel L. Brady,

Nehal A. Parikh,

Jonathan R. Dillman

Publication year - 2020

Publication title -

pediatric radiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.748

H-Index - 86

eISSN - 1432-1998

pISSN - 0301-0449

DOI - 10.1007/s00247-020-04854-3

Subject(s) - medicine , magnetic resonance elastography , receiver operating characteristic , magnetic resonance imaging , neuroradiology , elastography , chronic liver disease , radiology , ultrasound , cirrhosis , neurology , psychiatry

Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research