9 accepted papers
Graph anomaly detection (GAD) aims to identify nodes that exhibit significant deviations from expected structural or attribute patterns, and has garnered increasing attention in recent years. Recent approaches for GAD have predominantly focused on local inconsistency mining, which refers to the diff
Graph anomaly detection (GAD), which aims to identify patterns that deviate significantly from normal nodes in attributed networks, is widely used in financial fraud, cybersecurity, and bioinformatics. The paradigms of jointly optimizing contrastive learning and reconstruction learning have shown si
Anomaly detection on attributed graphs has applications in various domains such as finance and email spam detection, thus gaining substantial attention. Distributed scenarios can also involve issues related to anomaly detection in attribute graphs, such as in medical scenarios. However, most of the…
Subgraph representation learning has attracted growing interest due to its wide applications in various domains. However, existing methods primarily focus on local neighborhood structures while overlooking the significant impact of global structural information, in particular the influence of multi-…
Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and…
Despite the great potential in capturing long-range dependency, one rarely-explored underlying issue of transformer in medical image segmentation is attention collapse, making it often degenerate into a bypass module in CNN-Transformer hybrid architectures. This is due to the high computational comp…
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the perfo…
Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imaging quality across various institutions, often attributed to equipment malfunction…
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated label noise, especially in medical scenarios. In this…