27 citations · 181 across the 39 of their papers we have counts for
6 papers · 1 filter
Geometrically Guided Integrated Gradients
Md Mahfuzur Rahman, Noah Lewis, Sergey Plis
Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual…
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…
Learnt dynamics generalizes across tasks, datasets, and populations
U. Mahmood, M. M. Rahman, A. Fedorov +3
Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional app…
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
Alvaro Ulloa, Sergey Plis, Vince Calhoun
The wide variety of brain imaging technologies allows us to exploit information inherent to different data modalities. The richness of multimodal datasets may increase predictive p…
Almost instant brain atlas segmentation for large-scale studies
Alex Fedorov, Eswar Damaraju, Vince Calhoun +1
Large scale studies of group differences in healthy controls and patients and screenings for early stage disease prevention programs require processing and analysis of extensive mu…
End-to-end learning of brain tissue segmentation from imperfect labeling
Alex Fedorov, Jeremy Johnson, Eswar Damaraju +3
Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue change…