Artificial Intelligence Medical Compendium

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage ML mod... read more 

Early detection and accurate classification of retinal diseases, such as diabetic retinopathy (DR) and age-related macular degeneration (AMD), are essential to preventing vision loss and improving patient outcomes. Traditional methods for analyzing r... read more 

Extracellular vesicles, particularly exosomes, are emerging as powerful tools in cancer research due to their role in intercellular communication and their capacity to reflect the molecular composition of their originating cells. Multi-omic approache... read more 

BACKGROUND: Any risk behaviour may result in a negative outcome. This highly depends on the complex interplay of emotions and an individual's perception of risk. AI and ML can study the biological signature and speech of individuals, which can help c... read more 

Surface-enhanced Raman spectroscopy (SERS) is a powerful, label-free technique for pathogen detection; however, its broader adoption in clinical diagnostics is hindered by inconsistent spectral quality across portable and laboratory-grade instruments... read more 

Deep incremental hashing can generate hash codes incrementally for new classes, while keeping the existing ones unchanged. Existing methods typically allocate fixed code lengths to all classes, causing the entire Hamming space occupied by existing cl... read more 

This paper presents a fixed-time learning-based dynamic event-triggered control framework to address the optimal tracking control problem in robotic systems with the prescribed performance constraints. In many practical scenarios, the states of robot... read more 

Graph neural networks (GNNs) are prominent for their effectiveness in processing graph-structured data for semi-supervised node classification tasks. Most existing GNNs perform message passing directly based on the observed graph structure. However, ... read more 

Few-shot relation classification aims to distinguish different relation classes from extremely limited annotated data. Most existing methods primarily use prototype networks to construct a prototypical representation, classifying the instance by comp... read more 

The intricate and efficient information processing of the human brain, driven by spiking neural interactions, has led to the development of spiking neural networks (SNNs) as a cutting-edge neural network paradigm. Unlike traditional artificial neural... read more