The integration of artificial intelligence into biomedical image analysis represents a paradigm shift in clinical decision support and surgical informatics. Laparoscopic cholecystectomy (LC) is a widely performed minimally invasive procedure but remains technically challenging due to limited visibility, anatomical variations, and the risk of complications such as bile duct injury. Artificial intelligence (AI), particularly deep learning (DL), has shown great potential for improving intraoperative understanding through automated surgical video analysis. This review summarizes recent advances in DL-based LC video analysis, focusing on semantic segmentation and surgical action or phase recognit... More >

Resting-state EEG offers a low-cost, non-invasive biomarker for Alzheimer's and Parkinson's diseases, yet most deep learning models fail to generalize to new patients due to subject-dependent evaluation protocols, isolated frequency-band processing, and the neglect of cross-frequency interactions. We propose CFGT-Net, a Cross-Frequency Graph-Transformer Network. For each EEG epoch, five canonical bands are processed by a graph attention encoder on a learnable phase-lag index adjacency to produce spatial embeddings. A cross-frequency coupling (CFC) attention models band interactions, a temporal transformer tracks their evolution across epochs, and a correlation-alignment loss enforces subject... More >