9 accepted papers
Deep Riemannian networks provide a powerful framework for Electroencephalography (EEG) decoding, but their practical applications are severely constrained. Accurately decoding EEG signals requires modeling complex temporal dynamics across multiple rhythms, which results in high-dimensional Riemannia…
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, current ICL models for medical imaging remain limited in two critical aspects: they cannot simultaneously achieve high-fi
Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task, and flow information, which captures fine-grained temporal dynamics. However, existing deep decoding models typically…
Research on neural dynamics indicates that cognitive processes are driven by metastable state transitions in the brain, which highlights the temporally discontinuous nature of brain activity. However, many existing methods fail to account for this discontinuity, limiting their effectiveness in model…
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…
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks…
The multi-modal neuroimage study has dramatically facilitated disease diagnosis. Tensor-based methods are commonly used to represent multi-modal data as multi-dimensional arrays and usually implement matrix decomposition. These methods can be seen as a linear algebraic way for the lossy compression…