Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning

Abstract Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to...

Full description

Bibliographic Details
Main Authors:	Zhou, Xiao (Author), Qiu, Shangran (Author), Joshi, Prajakta S (Author), Xue, Chonghua (Author), Killiany, Ronald J (Author), Mian, Asim Z (Author), Chin, Sang (Author), Au, Rhoda (Author), Kolachalama, Vijaya B (Author)
Other Authors:	Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format:	Article
Language:	English
Published:	BioMed Central, 2022-07-15T20:36:32Z.
Subjects:	Article
Online Access:	Get fulltext

by Zhou, Xiao, Qiu, Shangran, Joshi, Prajakta S, Xue, Chonghua, Killiany, Ronald J, Mian, Asim Z, Chin, Sang P, Au, Rhoda, Kolachalama, Vijaya B
Published 2021

Get fulltext

Article

Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning

Similar Items