Kayhan Batmanghelich

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Magnetic resonance imaging (MRI) patterns were examined together with cerebrospinal fluid (CSF) biomarkers in serial scans of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI). The SPARE-AD score, summarizing brain atrophy patterns, was tested as a predictor of short-term conversion to Alzheimer's disease(More)
We investigate the potential of shape information in assisting the computer-aided diagnosis of Alzheimer’s disease and its prodromal stage of mild cognitive impairment. We employ BrainPrint to obtain an extensive characterization of the shape of brain structures. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by(More)
APPROXIMATE INFERENCE FOR DETERMINANTAL POINT PROCESSES Jennifer Gillenwater Ben Taskar Emily Fox In this thesis we explore a probabilistic model that is well-suited to a variety of subset selection tasks: the determinantal point process (DPP). DPPs were originally developed in the physics community to describe the repulsive interactions of fermions. More(More)
Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In(More)
In 2017, the ImageCLEF benchmark proposed a task based on CT (Computed Tomography) images of patients with tuberculosis (TB). This task was divided into two subtasks: multi-drug resistance prediction, and TB type detection. In this work we present a graph-model of the lungs capable of characterizing TB patients with different lung problems. This graph(More)
In this work we present our participation in the ImageCLEF 2017 tuberculosis task. The task consists of detecting five tuberculosis (TB) types and predicting drug resistance from lung CT (Computed Tomography) volumes. Our approach is based on a previously developed non-parametric method. Tested on CT images of Chronic Obstructive Pulmonary Disease (COPD)(More)
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance. In(More)
We propose novel finite-dimensional spaces of well-behaved Rn → Rn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and(More)
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