Artificial Intelligence Medical Compendium

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

Showing 45,781 to 45,790 of 224,055 articles

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information.

IEEE transactions on neural networks and learning systems Mar 1, 2026
In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From the perspective of spatial information, empty graph-structured... read more 

Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.

Journal of thoracic imaging Mar 1, 2026
PURPOSE: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic solid cancers. MATERIALS AND METHODS: A total of 231 patients with mediastinal lymphadenopathy were s... read more 

Improving Model Fusion by Training-Time Neuron Alignment With Fixed Neuron Anchors.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Model fusion aims to integrate several deep neural network (DNN) models' knowledge into one by fusing parameters, and it has promising applications, such as improving the generalization of foundation models and parameter averaging in federated learni... read more 

A Comprehensive Survey on Evidential Deep Learning and its Applications.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However, uncertainty estimation metho... read more 

Single-Domain Generalization via Path Flatness-Aware Optimization of Loss Landscapes.

IEEE transactions on neural networks and learning systems Mar 1, 2026
Domain generalization (DG) methods traditionally rely on multiple source domains to achieve the robust performance across unseen target domains. However, single-DG (SDG) presents a more practical paradigm by learning from a single source domain, addr... read more 

PGFormer: A Prototype-Graph Transformer for Incomplete Multiview Clustering.

IEEE transactions on neural networks and learning systems Mar 1, 2026
Incomplete multiview clustering (IMVC) faces significant challenges due to missing data and inherent view discrepancies. While deep neural networks offer powerful representation learning capabilities for IMVC, existing methods often overlook view div... read more 

Toward Better Generalization Bounds of Stochastic Optimization for Nonconvex Learning.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Stochastic optimization is the workhorse behind the success of many machine learning algorithms. The existing theoretical analysis of stochastic optimization mainly focuses on the behavior on the training dataset or requires a convexity assumption. I... read more 

CHOROIDAL VASCULARITY INDEX, RETINAL VASCULARITY, AND HEMOGLOBIN LEVELS IN PEDIATRIC SICKLE CELL MACULOPATHY.

Retina (Philadelphia, Pa.) Mar 1, 2026
PURPOSE: To evaluate the choroidal vascularity index (CVI) in pediatric patients with sickle cell disease (SCD) and its associations with retinal thickness and vasculature. METHODS: This is a retrospective case series of children with sickle cell dis... read more 

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.

Journal of thoracic imaging Mar 1, 2026
PURPOSE: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with a focus on small airway disease (SAD) and its correlation with plethysmography. MATERIALS AND METHOD... read more 

Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models.

IEEE transactions on pattern analysis and machine intelligence Mar 1, 2026
Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situations. Therefore, vision models have to behave in a robust way to disturbances such as noise or blur.... read more 
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