AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 141 to 150 of 2841 articles

Clear Filters

Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed i...

Break Adhesion: Triple adaptive-parsing for weakly supervised instance segmentation.

Neural networks : the official journal of the International Neural Network Society
Weakly supervised instance segmentation (WSIS) aims to identify individual instances from weakly supervised semantic segmentation precisely. Existing WSIS techniques primarily employ a unified, fixed threshold to identify all peaks in semantic maps. ...

Pan-sharpening via Symmetric Multi-Scale Correction-Enhancement Transformers.

Neural networks : the official journal of the International Neural Network Society
Pan-sharpening is a widely employed technique for enhancing the quality and accuracy of remote sensing images, particularly in high-resolution image downstream tasks. However, existing deep-learning methods often neglect the self-similarity in remote...

Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.

Neural networks : the official journal of the International Neural Network Society
The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the pro...

What is the impact of discrete memristor on the performance of neural network: A research on discrete memristor-based BP neural network.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks are receiving increasing attention from researchers. However, with the advent of big data era, artificial neural networks are limited by the Von Neumann architecture, making it difficult to make new breakthroughs in hardwar...

SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.

Neural networks : the official journal of the International Neural Network Society
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion class...

FxTS-Net: Fixed-time stable learning framework for Neural ODEs.

Neural networks : the official journal of the International Neural Network Society
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly pred...

COSTA: Contrastive Spatial and Temporal Debiasing framework for next POI recommendation.

Neural networks : the official journal of the International Neural Network Society
Current research on next point-of-interest (POI) recommendation focuses on capturing users' behavior patterns residing in their mobility trajectories. However, the learning process will inevitably cause discrepancies between the recommendation and in...

A prompt tuning method based on relation graphs for few-shot relation extraction.

Neural networks : the official journal of the International Neural Network Society
Prompt-tuning has recently proven effective in addressing few-shot tasks. However, task resources remain severely limited in the specific domain of few-shot relation extraction. Despite its successes, prompt-tuning faces challenges distinguishing bet...

Sample-efficient and occlusion-robust reinforcement learning for robotic manipulation via multimodal fusion dualization and representation normalization.

Neural networks : the official journal of the International Neural Network Society
Recent advances in visual reinforcement learning (visual RL), which learns from high-dimensional image observations, have narrowed the gap between state-based and image-based training. However, visual RL continues to face significant challenges in ro...