106 citations · 159 across the 10 of their papers we have counts for
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Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Junhao Wen, Mathilde Antoniades, Zhijian Yang +4
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neu…
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Rongguang Wang, Vishnu Bashyam, Zhijian Yang +10
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called…
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
Zhijian Yang, Junhao Wen, Christos Davatzikos
A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric d…
Subtyping brain diseases from imaging data
Junhao Wen, Erdem Varol, Zhijian Yang +5
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to t…
Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: dimensional representations of Alzheimer's Disease
Zhijian Yang, Ilya M. Nasrallah, Haochang Shou +8
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a…