132 citations · 298 across the 13 of their papers we have counts for
13 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…
Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram +7
Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of thei…
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation
Vikramjit Mitra, Hsiang-Yun Sherry Chien, Vasudha Kowtha +2
Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years. While accurate estimation…
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…
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Joseph Y. Cheng, Feiyu Chen, Christopher Sandino +3
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolut…
Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones
David Y Zeng, Jamil Shaikh, Dwight G Nishimura +2
Purpose: Off-resonance artifact correction by deep-learning, to facilitate rapid pediatric body imaging with a scan time efficient 3D cones trajectory. Methods: A residual convolut…
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
Joseph Y. Cheng, Feiyu Chen, Marcus T. Alley +2
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering. For many applications, sensing me…
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng +8
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it a…
Development and Clinical Implementation of Next Generation Very Light Weight and Extremely Flexible Receiver Arrays for Pediatric MRI
Shreyas S. Vasanawala, Robert Stormont, Scott Lindsay +8
We develop a novel next generation light-weight highly flexible pediatric coil array, combine it with a high-density pediatric posterior array or conventional posterior phased arra…