4 citations · 6 across the 8 of their papers we have counts for
3 papers · 1 filter
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features
Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan +1
Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. In our recent work, we have…
Predicting Brain Age using Transferable coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven…
coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the p…