6 citations · 9 across the 5 of their papers we have counts for
6 papers
Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images
Yuedi Zhang, Zhixiang Xia, Guosheng Yin +1
Multi-Instance Learning (MIL) is pivotal for analyzing complex, weakly labeled datasets, such as whole-slide images (WSIs) in computational pathology, where bags comprise unordered…
Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph
Lu Wei, Yi Huang, Guosheng Yin +3
We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data from diffus…
Source-Aware Embedding Training on Heterogeneous Information Networks
Tsai Hor Chan, Chi Ho Wong, Jiajun Shen +1
Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing…
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
Tsai Hor Chan, Fernando Julio Cendra, Lan Ma +2
Graph-based methods have been extensively applied to whole-slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different enti…
Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network
Min Hu, Zhizhong Tan, Bin Liu +1
This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural netw…
Asymmetric Graph Representation Learning
Zhuo Tan, Bin Liu, Guosheng Yin
Despite the enormous success of graph neural networks (GNNs), most existing GNNs can only be applicable to undirected graphs where relationships among connected nodes are two-way s…