AIMC Topic: Multimodal Imaging

Clear Filters Showing 281 to 290 of 311 articles

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Human brain mapping
Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging mod...

A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.

Translational vision science & technology
PURPOSE: To propose and validate a meta-learning approach for detecting retinal vein occlusion (RVO) from multimodal images with only a few samples.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Translational vision science & technology
PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia a...

Do we empathize humanoid robots and humans in the same way? Behavioral and multimodal brain imaging investigations.

Cerebral cortex (New York, N.Y. : 1991)
Humanoid robots have been designed to look more and more like humans to meet social demands. How do people empathize humanoid robots who look the same as but are essentially different from humans? We addressed this issue by examining subjective feeli...

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

JAMA cardiology
IMPORTANCE: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Seve...

Classification of Artifacts in Multimodal Fused Images using Transfer Learning with Convolutional Neural Networks.

Current medical imaging
INTRODUCTION: Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation tec...

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

Journal of X-ray science and technology
BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression ...

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Recent advances in inflammation & allergy drug discovery
BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising...

Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration.

Acta ophthalmologica
Geographic atrophy (GA) secondary to age-related macular degeneration is among the most common causes of irreversible vision loss in industrialized countries. Recently, two therapies have been approved by the US FDA. However, given the nature of thei...