Artificial Intelligence Medical Compendium

Polyp segmentation is critical in medical image analysis. Traditional methods, while capable of producing precise outputs in well-defined regions, often struggle with blurry or ambiguous areas in medical images, which can lead to errors in clinical d... read more 

A key challenge in Nearshore Infrared Video Super-resolution (NIVSR) is the limited high-frequency foreground information. The most common approach is to fuse frames in order to learn cross-temporal information. However, existing methods struggle to ... read more 

Subgraph federated learning (subgraph-FL) is a distributed machine learning paradigm enabling cross-client collaborative training of graph neural networks (GNNs). However, real-world subgraph-FL scenarios often face subgraph heterogeneity problem, i.... read more 

Learning accurate dynamics models is crucial for model-based reinforcement learning. Gaussian processes (GPs), as a probabilistic modeling approach, have been widely used for dynamical system modeling. However, standard GPs are designed for single-ou... read more 

INTRODUCTION: Advances in artificial intelligence offer the promise of automated analysis of optical coherence tomography (OCT) scans to detect ocular complications from anticancer drug therapy. To explore how such AI outputs are interpreted in clini... read more 

How is visual stability maintained across saccades? One theory poses the visual system has an underlying assumption that the visual world has not changed during the saccade, and scrutinization of trans-saccadic memory occurs only when there is strong... read more 

Catalytic promiscuity in enzymes refers to their ability to catalyze multiple chemically distinct reactions in addition to their native activity. The increasing discovery of additional enzymes exhibiting catalytic promiscuity has underscored the sign... read more 

Large language models (LLMs) have taken the natural language processing (NLP) domain by storm, and their transformative momentum has surged into the domain of education, giving rise to a nascent wave of education-tailored LLMs. Despite their potentia... read more 

A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and comput... read more 

Whole slide pathological images have shown significant potential for patient prognostication. Graph representation learning provides a robust framework for in-depth analysis of whole-slide images to construct predictive models. In this study, we intr... read more