16 citations · 35 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
Arjun D Desai, Andrew M Schmidt, Elka B Rubin +9
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and d…