Federated Brain Graph Evolution Prediction using Decentralized Connectivity Datasets with Temporally-varying Acquisitions.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3225083
Abstract: Foreseeing the evolution of brain connectivity between anatomical regions from a baseline observation can propel early disease diagnosis and clinical decision making. Such task becomes challenging when learning from multiple decentralized datasets with missing timepoints… read more here.
Keywords: connectivity; gnn; brain; acquisitions federated ... See more keywords
CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2023.3249343
Abstract: Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing geometric and hierarchically structured… read more here.
Keywords: topology; cell; gnn; level ... See more keywords
Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3087000
Abstract: Online handwritten diagram recognition (OHDR) has attracted considerable attention for its potential applications in many areas, but it is a challenging task due to the complex 2D structure, writing style variation, and lack of annotated… read more here.
Keywords: recognition; symbol; gnn; symbol segmentation ... See more keywords
Optimizing Graph Neural Network With Multiaspect Hilbert-Schmidt Independence Criterion.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3171419
Abstract: The graph neural network (GNN) has demonstrated its superior power in various data mining tasks and has been widely applied in diversified fields. The core of GNN is the aggregation and combination functions, and mainstream… read more here.
Keywords: neural network; network; gnn; graph neural ... See more keywords
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection
Sign Up to like & getrecommendations! Published in 2021 at "Applied Sciences"
DOI: 10.3390/app11125656
Abstract: Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art… read more here.
Keywords: detection; gnn; graph neural; fraud detection ... See more keywords
FUSION: Measuring Binary Function Similarity with Code-Specific Embedding and Order-Sensitive GNN
Sign Up to like & getrecommendations! Published in 2022 at "Symmetry"
DOI: 10.3390/sym14122549
Abstract: Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic… read more here.
Keywords: gnn; similarity; function similarity; order sensitive ... See more keywords