With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

In modern neuroscience and clinical study, neuroscientists and clinicians often use non-
invasive imaging techniques to validate theories and computational models, observe brain …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Functional connectivity (FC) between regions of the brain can be assessed by the degree of
temporal correlation measured with functional neuroimaging modalities. Based on the fact …

Interpretability of machine learning models is critical to scientific understanding, AI safety, as
well as debugging. Attribution is one approach to interpretability, which highlights input …

We present an interpretable deep model for fine-grained visual recognition. At the core of
our method lies the integration of region-based part discovery and attribution within a deep …

Deep learning analyses have offered sensitivity leaps in detection of cognition-related
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …

Graph neural networks (GNN) rely on graph operations that include neural network training
for various graph related tasks. Recently, several attempts have been made to apply the …

Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …

Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …