Graph Convolutional Networks for Multi-modality Medical Imaging ...
WEBFeb 17, 2022 · We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice.
Graph neural networks for image‐guided disease diagnosis: A …
WEBJun 27, 2023 · The knowledge graph illustrates the relationships between real-world entities, and a graph structure is used to visualize the information stored in the graph. To summarize, irregular graph structures in medical images contribute to disease diagnosis.
Graph and Hypergraph Learning in Medical Image Analysis
WEBOct 12, 2023 · This tutorial seeks to draw attention to the current developments in graph learning and high-order relations via hypergraph learning for medical image analysis. The main objective is to explore the vast literature and recent developments in the area of graph and hypergraph learning for medical data. We will provide a discussion on the state-of ...
Graph-Based Deep Learning for Medical Diagnosis and Analysis: …
WEBWe introduce and discuss diverse graph frameworks proposed for medical diagnosis and their specific applications. We cover work for biomedical imaging applications using graph networks combined with deep learning techniques.
Multi-Modal Graph Learning for Disease Prediction - IEEE Xplore
WEBMar 14, 2022 · MMGL is a framework that uses graphs to capture the latent structure and correlation of multi-modal medical data for disease prediction. It integrates modality-aware representation learning and adaptive graph learning, and achieves better performance than existing methods.
Graph Learning in Medical Imaging - Springer
WEBThe GLMI 2019 proceedings focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging. Topics of interests include, but are not limited to graph-based methods.
BrainGNN: Interpretable Brain Graph Neural Network for fMRI …
WEBDec 1, 2021 · BrainGNN is a graph neural network (GNN) framework that analyzes functional magnetic resonance images (fMRI) and discovers neurological biomarkers. It uses novel ROI-aware graph convolutional layers, ROI-selection pooling layers, and regularization terms to select salient brain regions and clusters for prediction tasks.
WEBvarious medical tests that capture the patient’s functional and anatomical structures through diagnostic imaging data such as functional magnetic resonance imaging (fMRI), magnetic resonance imaging (MRI), computed tomography (CT), ul-trasound (US) and X-ray; and other diagnostic tools include electroenchephalogram (EEG).
Adaptive Graph Convolutional Networks for Medical Image …
WEBIn this paper, we present a graph-based approach replacing Transformer design to capture long-range dependencies and reduce computational cost. Our proposed framework achieves competitive performance using publicly available dataset Synapse.
Graph matching survey for medical imaging: On the way to …
WEBJun 1, 2022 · Graph matching is a fundamental part of many medical imaging applications. It serves to extract and compare information of images acquired in different points in time, but it can also serve other purposes like enabling intraoperative guidance.