1 citations · 1 across the 10 of their papers we have counts for
10 papers
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Canberk Ekmekci, Mujdat Cetin
Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian n…
LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring
Shadi Sartipi, Mie Andersen, Natalie Hauglund +4
Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensive nature of manual sleep scorin…
Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification
Canberk Ekmekci, Tekin Bicer, Zichao Wendy Di +2
Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. One main challenge is that widely used iterativ…
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model
Shadi Sartipi, Mujdat Cetin
Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often su…
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification
Shadi Sartipi, Mujdat Cetin
Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer…
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition
Shadi Sartipi, Mujdat Cetin
Although deep learning-based algorithms have demonstrated excellent performance in automated emotion recognition via electroencephalogram (EEG) signals, variations across brain sig…
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition
Shadi Sartipi, Mastaneh Torkamani-Azar, Mujdat Cetin
Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design a…
MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem. Classical methods for magnetic resonance elastography (M…
Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of int…
A statistical framework for model-based inverse problems in ultrasound elastography
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Model-based computational elasticity imaging of tissues can be posed as solving an inverse problem over finite elements spanning the displacement image. As most existing quasi-stat…