Improved amyloid burden quantification with nonspecific estimates using deep learning

Improved amyloid burden quantification with nonspecific estimates using deep learning

10.1007/s00259-020-05131-z

Saved in:

Bibliographic Details
Main Authors:	Liu, Haohui, Nai, Ying-Hwey, Saridin, Francis, Tanaka, Tomotaka, O' Doherty, Jim, Hilal, Saima, Gyanwali, Bibek, Chen, Christopher P, Robins, Edward G, Reilhac, Anthonin
Other Authors:	SOLAR ENERGY RESEARCH INST OF S'PORE
Format:	Article
Language:	English
Published:	SPRINGER 2022
Subjects:	Science & Technology Life Sciences & Biomedicine Radiology, Nuclear Medicine & Medical Imaging Alzheimer's disease Amyloid Positron emission tomography (PET) Quantification Deep learning Nonspecific uptake COGNITIVE IMPAIRMENT BRAIN SEGMENTATION PERFORMANCE DISEASE
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/218776
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore
Language:	English

Description
Summary:	10.1007/s00259-020-05131-z