Artificial Intelligence Medical Compendium

Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented neural methods. However, existing methods typically improve compressio... read more 

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of t... read more 

Despite the strong performance of matrix-based second-order optimization methods, their high computational and storage costs make them challenging to scale to large machine learning problems. To mitigate these limitations, we propose a stochastic sym... read more 

The Kolmogorov-Arnold Network (KAN) has emerged as a promising neural network architecture for small-scale AI Science applications. We explore the capability of KAN in representing ridge functions through the theoretical framework of the Kolmogorov-A... read more 

Current Hierarchical Reinforcement Learning (HRL) algorithms excel in long-horizon sequential decision-making tasks but still face two challenges: delay effects and spurious correlations. To address them, we propose a causal HRL approach called D3HRL... read more 

Brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) is the predominant method for studying brain functional organization of infants. Predicting the full dynamic developmental trajectory of infant FC from existin... read more 

BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) models for electrocardiogram (ECG) interpretation rely on large, diverse datasets, but existing clinical datasets are often skewed, overrepresenting normal rhythms and lacking rare pathologies. T... read more 

A far-ultraviolet/peracetic acid (UV222/PAA) disinfection process was developed in this study to investigate the transformation mechanisms of dissolved organic matter (DOM) in wastewater, via an integrated approach combining comprehensive molecular c... read more 

BACKGROUND: Automated breast ultrasound (ABUS) shows potential for breast cancer diagnosis but faces tumor segmentation challenges due to limited annotated data and the tendency of deep convolution neural networks to introduce irrelevant features, hi... read more 

This study aims to explore the lymphangiogenesis (LG)-related diagnostic markers of abdominal aortic aneurysm (AAA) through bioinformatics, as well as the alteration of the regional lymphatic system during the progression of AAA and the influence of ... read more