Search Results for author: Ukash Nakarmi

Found 6 papers, 1 papers with code

Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials

1 code implementation1 Feb 2024 Shakil Rafi, Joshua Lee Padgett, Ukash Nakarmi

We make the case for neural network objects and extend an already existing neural network calculus explained in detail in Chapter 2 on \cite{bigbook}.

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery

no code implementations27 Dec 2018 Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI).

Dimensionality Reduction

Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case

no code implementations27 Feb 2020 Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying

This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem.

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images

no code implementations30 Sep 2020 Edgar A. Rios Piedra, Morteza Mardani, Frank Ong, Ukash Nakarmi, Joseph Y. Cheng, Shreyas Vasanawala

Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.

Segmentation

Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI

no code implementations16 Oct 2021 Abhijit Baul, Nian Wang, Choyi Zhang, Leslie Ying, Yuchou Chang, Ukash Nakarmi

Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure.

Cannot find the paper you are looking for? You can Submit a new open access paper.