Ensuring that artificial intelligence (AI) tools in healthcare operate safely and effectively requires robust evaluation within realistic clinical contexts. Traditional evaluation methods often rely on standardized benchmarks that fail to capture the full complexity of patient care, while manually curating a dataset for a specific deployment scenario can be time-consuming and limiting.
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data.
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data.