AI Medical Compendium Topic

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CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation.

Computers in biology and medicine
Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular pathologies. Current methods have limitations in feature fusion and face challenges in simultaneously capturing global and local features from fundus images. To...

A Mood Semantic Awareness Model for Emotional Interactive Robots.

Sensors (Basel, Switzerland)
The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human-machine dialogue systems. In this study, the Icc_dialogue model...

MSDCNN: A multiscale dilated convolution neural network for fine-grained 3D shape classification.

Neural networks : the official journal of the International Neural Network Society
Multi-view deep neural networks have shown excellent performance on 3D shape classification tasks. However, global features aggregated from multiple views data often lack content information and spatial relationship, which leads to difficult identifi...

CMBEE: A constraint-based multi-task learning framework for biomedical event extraction.

Journal of biomedical informatics
OBJECTIVE: Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong sema...

Development and application of Chinese medical ontology for diabetes mellitus.

BMC medical informatics and decision making
OBJECTIVE: To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.

SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules.

BMC medical imaging
BACKGROUND: Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on...

Contrastive learning of graphs under label noise.

Neural networks : the official journal of the International Neural Network Society
In the domain of graph-structured data learning, semi-supervised node classification serves as a critical task, relying mainly on the information from unlabeled nodes and a minor fraction of labeled nodes for training. However, real-world graph-struc...

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone.

Local structure-aware graph contrastive representation learning.

Neural networks : the official journal of the International Neural Network Society
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackles the label problem effectively, mainly focus on the feature inform...

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract.

Biomedical physics & engineering express
. The objectives of the proposed work are twofold. Firstly, to develop a specialized light weight CRPU-Net for the segmentation of polyps in colonoscopy images. Secondly, to conduct a comparative analysis of the performance of CRPU-Net with implement...