PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety.

The issue of obstacle avoidance and safety for visually impaired individuals has been a major topic of research. However, complex street environments still pose significant challenges for blind obstacle detection systems. Existing solutions often fail to provide real-time, accurate obstacle avoidance decisions.

Targeting Chondrocyte Hypertrophy as Strategies for the Treatment of Osteoarthritis.

Osteoarthritis (OA) is a common joint disease characterized by pain and functional impairment, which severely impacts the quality of life of middle-aged and elderly individuals. During normal bone development, chondrocyte hypertrophy is a natural physiological process. However, in the progression of OA, chondrocyte hypertrophy becomes one of its key pathological features.

Graph anomaly detection based on hybrid node representation learning.

Anomaly detection on graph data has garnered significant interest from both the academia and industry. In recent years, fueled by the rapid development of Graph Neural Networks (GNNs), various GNNs-based anomaly detection methods have been proposed and achieved good results. However, GNNs-based methods assume that connected nodes have similar classes and features, leading to issues of class inconsistency and semantic inconsistency in graph anomaly detection.

3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study.

Background: Besides tumorous information, synergistic liver parenchyma assessments may provide additional insights into the prognosis of hepatocellular carcinoma (HCC). This study aimed to investigate whether 3D synergistic tumor-liver analysis could improve the prediction accuracy for HCC prognosis.

Methods: A total of 422 HCC patients from six centers were included.