• Publications
  • Influence
Lane detection and tracking using B-Snake
TLDR
We proposed a B-Snake based lane detection and tracking algorithm without any cameras' parameters. Expand
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Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
TLDR
In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-Modal data. Expand
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Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI),Expand
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Infant Brain Atlases from Neonates to 1- and 2-Year-Olds
Background Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the dataExpand
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Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
TLDR
High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. Expand
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Contour Knowledge Transfer for Salient Object Detection
TLDR
We introduce a deep network architecture, namely Contour-to-Saliency Network (C2S-Net), by grafting a new branch onto a well-trained contour detection network. Expand
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Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects
  • W. Gao, H. Zhu, +4 authors W. Lin
  • Biology, Medicine
  • Proceedings of the National Academy of Sciences
  • 21 April 2009
Several lines of evidence have implicated the existence of the brain's default network during passive or undirected mental states. Nevertheless, results on the emergence of the default network inExpand
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Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e.,Expand
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Longitudinal development of cortical and subcortical gray matter from birth to 2 years.
Very little is known about cortical development in the first years of life, a time of rapid cognitive development and risk for neurodevelopmental disorders. We studied regional cortical andExpand
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Hierarchical active shape models, using the wavelet transform
TLDR
This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform, to capture the full range of biological shape variability. Expand
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