Search Results for author:
Found 16 papers, 3 papers with code
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
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 by contributing to precision medicine.
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e. g., human supervision) and the underlying inference algorithms are closely interwined.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources.
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging
Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging.
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools in computer vision.
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM
We find that RapidPT achieves its best runtime performance on medium sized datasets ($50 \leq n \leq 200$), with speedups of 1. 5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT).
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Consider samples from two different data sources $\{\mathbf{x_s^i}\} \sim P_{\rm source}$ and $\{\mathbf{x_t^i}\} \sim P_{\rm target}$.
Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
On Statistical Analysis of Neuroimages With Imperfect Registration
A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases.
Statistical Inference Models for Image Datasets With Systematic Variations
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
Speeding up Permutation Testing in Neuroimaging
In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$.
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images
Linear regression is a parametric model which is ubiquitous in scientific analysis.
