10 citations · 24 across the 5 of their papers we have counts for
5 papers
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…
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun +22
Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms a…
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…
Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images
Zhijian Yang, Junhao Wen, Christos Davatzikos
Machine learning methods applied to complex biomedical data has enabled the construction of disease signatures of diagnostic/prognostic value. However, less attention has been give…