Search Results for author:
Found 6 papers, 1 papers with code
Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer, contributing to improved localization performance for the segmentation model.
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs.
Ranked #3 on
Node Classification
on Actor
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation
The generalization of the model is further verified on the WORD dataset.
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection
This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme.
Fast Community Detection based on Graph Autoencoder Reconstruction
With the rapid development of big data, how to efficiently and accurately discover tight community structures in large-scale networks for knowledge discovery has attracted more and more attention.
VGAER: Graph Neural Network Reconstruction based Community Detection
Community detection is a fundamental and important issue in network science, but there are only a few community detection algorithms based on graph neural networks, among which unsupervised algorithms are almost blank.
