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Ukash Nakarmi
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2020 – today
- 2024
- [j6]Ibsa Jalata
, Reeshad Khan
, Ukash Nakarmi
:
Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction. IEEE Access 12: 97621-97629 (2024) - [c15]Arpan Poudel, Ukash Nakarmi:
DeepLIR: Attention-Based Approach for Mask-Based Lensless Image Reconstruction. WACV (Workshops) 2024: 431-439 - [i6]Shakil Rafi, Joshua Lee Padgett, Ukash Nakarmi:
Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials. CoRR abs/2402.01058 (2024) - [i5]Reeshad Khan, John Gauch, Ukash Nakarmi:
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising. CoRR abs/2407.15799 (2024) - 2023
- [c14]Mamata Shrestha, Nian Wang, Ukash Nakarmi:
On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging. BHI 2023: 1-5 - [c13]Ibsa Jalata
, Ukash Nakarmi
:
When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging. ISVC (1) 2023: 370-381 - 2022
- [j5]Konstantinos Slavakis
, Gaurav N. Shetty, Loris Cannelli, Gesualdo Scutari
, Ukash Nakarmi, Leslie Ying
:
Kernel Regression Imputation in Manifolds Via Bi-Linear Modeling: The Dynamic-MRI Case. IEEE Trans. Computational Imaging 8: 133-147 (2022) - [c12]Quan Mai, Ukash Nakarmi, Miaoqing Huang:
BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset. BIBM 2022: 3852-3855 - 2021
- [c11]Yuchou Chang, Ukash Nakarmi:
Parallel MRI Reconstruction Using Broad Learning System. EMBC 2021: 2704-2707 - 2020
- [j4]Chaoyi Zhang, Tanzil Mahmud Arefin
, Ukash Nakarmi, Choong-Heon Lee, Hongyu Li, Dong Liang, Jiangyang Zhang
, Leslie Ying
:
Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. NeuroImage 210: 116584 (2020) - [j3]Gaurav N. Shetty
, Konstantinos Slavakis
, Abhishek Bose
, Ukash Nakarmi
, Gesualdo Scutari
, Leslie Ying
:
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery. IEEE Trans. Medical Imaging 39(3): 688-702 (2020) - [c10]Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying:
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case. EUSIPCO 2020: 1482-1486 - [c9]Jeffrey J. Ma
, Ukash Nakarmi, Cedric Yue Sik Kin, Christopher M. Sandino, Joseph Y. Cheng, Ali B. Syed, Peter Wei, John M. Pauly
, Shreyas S. Vasanawala
:
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges. ISBI 2020: 337-340 - [c8]Ukash Nakarmi, Joseph Y. Cheng, Edgar P. Rios, Morteza Mardani, John M. Pauly
, Leslie Ying
, Shreyas S. Vasanawala
:
Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging. ISBI 2020: 1056-1059 - [i4]Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying:
Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case. CoRR abs/2002.11885 (2020) - [i3]Edgar A. Rios Piedra, Morteza Mardani, Frank Ong, Ukash Nakarmi, Joseph Y. Cheng, Shreyas Vasanawala:
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images. CoRR abs/2010.00003 (2020)
2010 – 2019
- 2019
- [j2]Jingyuan Lyu
, Ukash Nakarmi
, Dong Liang
, Jinhua Sheng, Leslie Ying
:
KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction. IEEE Trans. Medical Imaging 38(1): 312-321 (2019) - [i2]Jeffrey J. Ma, Ukash Nakarmi, Cedric Yue Sik Kin, Christopher M. Sandino, Joseph Y. Cheng, Ali B. Syed, Peter Wei, John M. Pauly, Shreyas Vasanawala:
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges. CoRR abs/1912.02907 (2019) - 2018
- [b1]Ukash Nakarmi:
Kernel and Manifold Framework for Magnetic Resonance Imaging. University at Buffalo, New York, USA, 2018 - [c7]Ukash Nakarmi, Konstantinos Slavakis, Leslie Ying
:
MLS: Joint manifold-learning and sparsity-aware framework for highly accelerated dynamic magnetic resonance imaging. ISBI 2018: 1213-1216 - [i1]Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Leslie Ying, Gesualdo Scutari:
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery. CoRR abs/1812.10617 (2018) - 2017
- [j1]Ukash Nakarmi, Yanhua Wang, Jingyuan Lyu, Dong Liang
, Leslie Ying
:
A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI. IEEE Trans. Medical Imaging 36(11): 2297-2307 (2017) - [c6]Konstantinos Slavakis, Gaurav N. Shetty, Abhishek Bose, Ukash Nakarmi, Leslie Ying
:
Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery. CAMSAP 2017: 1-5 - [c5]Ukash Nakarmi, Konstantinos Slavakis, Jingyuan Lyu, Leslie Ying
:
M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging. ISBI 2017: 19-22 - 2016
- [c4]Ukash Nakarmi, Yihang Zhou
, Jingyuan Lyu, Konstantinos Slavakis, Leslie Ying
:
Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding. ISBI 2016: 510-513 - 2015
- [c3]Ukash Nakarmi, Yanhua Wang, Jingyuan Lyu, Leslie Ying
:
Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D). ISBI 2015: 331-334 - 2012
- [c2]Ukash Nakarmi, Nazanin Rahnavard:
BCS: Compressive sensing for binary sparse signals. MILCOM 2012: 1-5 - 2011
- [c1]Ukash Nakarmi, Nazanin Rahnavard:
Joint wideband spectrum sensing in frequency overlapping cognitive radio networks using distributed compressive sensing. MILCOM 2011: 1035-1040
Coauthor Index
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last updated on 2024-08-25 19:10 CEST by the dblp team
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