5 papers
Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Nikhil J. Dhinagar, Vidhi Chhatbar, Chirag Jagad +6
Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not merely from overfitting but from a…
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
Paul M. Thompson
How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress increasingly depends on predict…
Multi-modal Imputation for Alzheimer's Disease Classification
Abhijith Shaji, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modaliti…
Diffusion Bridge Models for 3D Medical Image Translation
Shaorong Zhang, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-w…
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis
Nikhil J. Dhinagar, Amit Singh, Saket Ozarkar +11
Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning invol…