132 citations · 249 across the 7 of their papers we have counts for
10 papers
MAEEG: Masked Auto-encoder for EEG Representation Learning
Hsiang-Yun Sherry Chien, Hanlin Goh, Christopher M. Sandino +1
Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstructi…
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Edgar A. Rios Piedra, Morteza Mardani, Frank Ong +3
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medi…
Subject-Aware Contrastive Learning for Biosignals
Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz +2
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challen…
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Elizabeth K. Cole, Joseph Y. Cheng, John M. Pauly +1
Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do…
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Jeffrey Ma, Ukash Nakarmi, Cedric Yue Sik Kin +6
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic qualit…
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
Christopher M. Sandino, Peng Lai, Shreyas S. Vasanawala +1
A novel neural network architecture, known as DL-ESPIRiT, is proposed to reconstruct rapidly acquired cardiac MRI data without field-of-view limitations which are present in previo…