11 citations · 15 across the 3 of their papers we have counts for
7 papers
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…
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…
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Yuankai Huo, Zhoubing Xu, Yunxi Xiong +7
Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging…
Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation
Yuankai Huo, Katherine Swett, Susan M. Resnick +2
Probabilistic atlases provide essential spatial contextual information for image interpretation, Bayesian modeling, and algorithmic processing. Such atlases are typically construct…
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Yuankai Huo, Zhoubing Xu, Katherine Aboud +6
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS)…
4D Multi-atlas Label Fusion using Longitudinal Images
Yuankai Huo, Susan M. Resnick, Bennett A. Landman
Longitudinal reproducibility is an essential concern in automated medical image segmentation, yet has proven to be an elusive objective as manual brain structure tracings have show…