Artificial Intelligence Medical Compendium

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

Showing 64,431 to 64,440 of 231,605 articles

From Physical Degradation Models to Task-Aware All-in-One Image Restoration

arXiv Jan 15, 2026
All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning module... read more 
Computer Vision and Pattern Recognition

ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and TEst-time Generative Adaptation

arXiv Jan 15, 2026
We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation. Prior works rely either on a 3D data prior or a 2D generative prior to compensate for missing visua... read more 
Computer Vision and Pattern Recognition

Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning

arXiv Jan 15, 2026
Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and generalizable detection methods from electrocardiogram (ECG) recordings. Although deep learning has ... read more 
Quantitative Methods

Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy

arXiv Jan 15, 2026
The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer vision, requiring approaches that eff... read more 
Image and Video Processing cs.CV q-bio.QM

DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset

arXiv Jan 15, 2026
Vision-Language Pre-training (VLP) models demonstrate strong performance across various downstream tasks by learning from large-scale image-text pairs through contrastive pretraining. The release of extensive English image-text datasets (e.g., COYO-7... read more 
Computer Vision and Pattern Recognition cs.AI

Hierarchical Refinement of Universal Multimodal Attacks on Vision-Language Models

arXiv Jan 15, 2026
Existing adversarial attacks for VLP models are mostly sample-specific, resulting in substantial computational overhead when scaled to large datasets or new scenarios. To overcome this limitation, we propose Hierarchical Refinement Attack (HRA), a mu... read more 
Computer Vision and Pattern Recognition cs.MM

Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

arXiv Jan 15, 2026
Recent progress in text-to-image (T2I) diffusion models (DMs) has enabled high-quality visual synthesis from diverse textual prompts. Yet, most existing T2I DMs, even those equipped with large language model (LLM)-based text encoders, remain text-pix... read more 
Computer Vision and Pattern Recognition

An analytic theory of convolutional neural network inverse problems solvers

arXiv Jan 15, 2026
Supervised convolutional neural networks (CNNs) are widely used to solve imaging inverse problems, achieving state-of-the-art performance in numerous applications. However, despite their empirical success, these methods are poorly understood from a t... read more 
Computer Vision and Pattern Recognition cs.LG

EvoMorph: Counterfactual Explanations for Continuous Time-Series Extrinsic Regression Applied to Photoplethysmography

arXiv Jan 15, 2026
Wearable devices enable continuous, population-scale monitoring of physiological signals, such as photoplethysmography (PPG), creating new opportunities for data-driven clinical assessment. Time-series extrinsic regression (TSER) models increasingly ... read more 
Machine Learning

PLGC: Pseudo-Labeled Graph Condensation

arXiv Jan 15, 2026
Large graph datasets make training graph neural networks (GNNs) computationally costly. Graph condensation methods address this by generating small synthetic graphs that approximate the original data. However, existing approaches rely on clean, super... read more 
Machine Learning
Popular Topics
Recent Journals