3 accepted papers
In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, li…
In practical medical image segmentation tasks, ensuring confidence calibration is crucial. However, medical image segmentation typically relies on hard labels (one-hot vectors), and when minimizing the cross-entropy loss, the model’s softmax predictions are compelled to align with hard labels, resul…
Recent advancements in fMRI-based brain disorder diagnosis have shown that graph neural networks (GNNs) have been state-of-the-art methods for brain network analysis. Among them, transductive and inductive learning can be exploited by GNN. Transductive graphs, such as population graphs, take each su…