6 citations · 10 across the 5 of their papers we have counts for
8 papers
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 Alzheimer's disease neurodegeneration from typical brain aging using machine learning
Gyujoon Hwang, Ahmed Abdulkadir, Guray Erus +18
Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. Machi…
Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline
Long Xie, Laura E. M. Wisse, Jiancong Wang +6
Deep learning (DL) is the state-of-the-art methodology in various medical image segmentation tasks. However, it requires relatively large amounts of manually labeled training data,…
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…
DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease
Mengjin Dong, Long Xie, Sandhitsu R. Das +5
Volume change measures derived from longitudinal MRI (e.g. hippocampal atrophy) are a well-studied biomarker of disease progression in Alzheimer's Disease (AD) and are used in clin…
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Vishnu M. Bashyam, Jimit Doshi, Guray Erus +24
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and…