Artificial Intelligence Medical Compendium

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

Showing 65,431 to 65,440 of 231,904 articles

Latest research

Hippocampus-centered structural covariance network reorganization in Alzheimer's disease: An individualized graph-based biomarker validated by machine learning.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2026
Alzheimer's disease (AD) is characterized by progressive brain network disintegration, yet quantifying this process at an individual level remains challenging. This study explores the potential of an individualized differential structural covariance ... read more 

NeuroDetour: A neural pathway transformer for uncovering structural-functional coupling mechanisms in human connectome.

Medical image analysis Jan 8, 2026
Although modern imaging methods enable in-vivo examination of connections between distinct brain areas, we still lack a comprehensive understanding of how anatomical structure underpins brain function and how spontaneous fluctuations in neural activi... read more 

EEG-based schizophrenia classification using attention-integrated deep convolutional networks.

Psychiatry research. Neuroimaging Jan 8, 2026
Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for which electroencephalography (EEG) provides a valuable non-invasive biomarker. In this study, we propose a convolutional attention-based deep learning... read more 

Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities.

Computer methods and programs in biomedicine Jan 8, 2026
BACKGROUND AND OBJECTIVE: Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early in... read more 

HeartUnloadNet: A cycle-consistent graph network with reduced supervision for predicting unloaded cardiac geometry from diastolic states.

Computer methods and programs in biomedicine Jan 8, 2026
BACKGROUND AND OBJECTIVE: The unloaded cardiac geometry, representing the zero-stress and zero-strain reference state of the heart, is fundamental for personalized biomechanical modeling of cardiac function. However, this state cannot be directly obs... read more 

A novel cross-domain fault diagnosis method for multi-condition industrial processes based on meta-domain adaptation with progressive meta-learning.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2026
Complex industrial processes are characterized by high dynamics, diverse operating conditions, and strong inter-system coupling, often leading to reduced production efficiency and product quality fluctuations. Employing advanced fault diagnosis techn... read more 

Two-hidden-layer ReLU neural networks and finite elements.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2026
We point out that (continuous or discontinuous) piecewise linear functions on a convex polytope mesh can be represented by two-hidden-layer ReLU neural networks in a weak sense. In addition, the numbers of neurons of the two hidden layers required to... read more 

AttCo: Attention-based co-Learning fusion of deep feature representation for medical image segmentation using multimodality.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2026
Accurate tissue segmentation is crucial for advancing healthcare, particularly in disease prediction and treatment planning. Precisely identifying abnormal tissue locations is a critical step for clinical analysis. While medical image segmentation in... read more 

Rapid spatio-temporal MR fingerprinting using physics-informed implicit neural representation.

Medical image analysis Jan 8, 2026
The potential of Magnetic Resonance Fingerprinting (MRF), which allows for rapid and simultaneous multi-parametric quantitative MRI, is often limited by severe aliasing artifacts caused by aggressive undersampling. Conventional MRF approaches typical... read more 

HDFLStyler: Hierarchical domain-invariant feature learning for source-free domain generalization.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2026
Source-Free Domain Generalization (SFDG) aims to generalize a model to unknown domains without using any specific source domain data. Currently, SFDG methods mainly use the vision-language large models to extract different style features from text pr... read more 

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