Artificial Intelligence Medical Compendium

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

Showing 42,951 to 42,960 of 223,853 articles

MM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluation

arXiv
Non-negative matrix factorisation (NMF) is a widely used tool for unsupervised learning and feature extraction, with applications ranging from genomics to text analysis and signal processing. Standard formulations of NMF are typically derived under G... read more 

Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis

arXiv
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 

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

arXiv
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 

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

arXiv
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 

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

arXiv
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 

Decoder-Free Distillation for Quantized Image Restoration

arXiv
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 

Grounding Synthetic Data Generation With Vision and Language Models

arXiv
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 

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

arXiv
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 

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

arXiv
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 

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

arXiv
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