1.7k citations · 1.8k across the 12 of their papers we have counts for
14 papers
High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
Zhuoyi Huang, Nutan Sahoo, Anamika Kumari +13
The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offer…
MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Armina Fani, Mike Doan, Isabelle Le +4
Deployment complexity and specialized hardware requirements hinder the adoption of deep learning models in neuroimaging. We present MindGrab, a lightweight, fully convolutional mod…
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…
Pipeline-Invariant Representation Learning for Neuroimaging
Xinhui Li, Alex Fedorov, Mrinal Mathur +4
Deep learning has been widely applied in neuroimaging, including predicting brain-phenotype relationships from magnetic resonance imaging (MRI) volumes. MRI data usually requires e…
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
Alex Fedorov, Eloy Geenjaar, Lei Wu +3
Self-supervised learning has enabled significant improvements on natural image benchmarks. However, there is less work in the medical imaging domain in this area. The optimal model…
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Alex Fedorov, Tristan Sylvain, Eloy Geenjaar +7
Sensory input from multiple sources is crucial for robust and coherent human perception. Different sources contribute complementary explanatory factors. Similarly, research studies…