Artificial Intelligence Medical Compendium

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

Showing 43,441 to 43,450 of 224,055 articles

Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis

arXiv Mar 10, 2026
Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identi... read more 
Machine Learning

A saccade-inspired approach to image classification using visiontransformer attention maps

arXiv Mar 10, 2026
Human vision achieves remarkable perceptual performance while operating under strict metabolic constraints. A key ingredient is the selective attention mechanism, driven by rapid saccadic eye movements that constantly reposition the high-resolution f... read more 
Computer Vision and Pattern Recognition

A Saccade-inspired Approach to Image Classification using Vision Transformer Attention Maps

arXiv Mar 10, 2026
Human vision achieves remarkable perceptual performance while operating under strict metabolic constraints. A key ingredient is the selective attention mechanism, driven by rapid saccadic eye movements that constantly reposition the high-resolution f... read more 
Computer Vision and Pattern Recognition

Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution

arXiv Mar 10, 2026
High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts. Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are... read more 
Computer Vision and Pattern Recognition

Decoder-Free Distillation for Quantized Image Restoration

arXiv Mar 10, 2026
Quantization-Aware Training (QAT), combined with Knowledge Distillation (KD), holds immense promise for compressing models for edge deployment. However, joint optimization for precision-sensitive image restoration (IR) to recover visual quality from ... read more 
Computer Vision and Pattern Recognition

Grounding Synthetic Data Generation With Vision and Language Models

arXiv Mar 10, 2026
Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics for synthetic data typically calculate latent feature similarity, which ... read more 
Computer Vision and Pattern Recognition cs.AI

X-GS: An Extensible Open Framework Unifying 3DGS Architectures with Downstream Multimodal Models

arXiv Mar 10, 2026
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, subsequently extending into numerous spatial AI applications. However, most existing 3DGS methods are isolated, focusing on specific domains such as online SLA... read more 
Computer Vision and Pattern Recognition cs.CL

Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs

arXiv Mar 10, 2026
Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditional Generative Adversarial Network (cGAN) framework for synthesizing ... read more 
Machine Learning physics.geo-ph

When to Lock Attention: Training-Free KV Control in Video Diffusion

arXiv Mar 10, 2026
Maintaining background consistency while enhancing foreground quality remains a core challenge in video editing. Injecting full-image information often leads to background artifacts, whereas rigid background locking severely constrains the model's ca... read more 
Computer Vision and Pattern Recognition cs.AI cs.ET eess.IV

GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation

arXiv Mar 10, 2026
There is growing interest in applying graph-based methods to Time Series Anomaly Detection (TSAD), particularly Graph Neural Networks (GNNs), as they naturally model dependencies among multivariate signals. GNNs are typically used as backbones in sco... read more 
Machine Learning cs.AI
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