Latest AI and machine learning research in practice management for healthcare professionals.
Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network f...
Biomedical ontologies, which comprehensively define concepts and relations for biomedical entities, are crucial for structuring and formalizing domain-specific information representations. Biomedical code mapping identifies similarity or equivalence between concepts from different ontologies. Obtaining high-quality mapping usually relies on automatic generation of unrefined mapping with ontology...
This study addresses the critical issue of reliability for AI-assisted medical diagnosis. We focus on the selection prediction approach that allows ...
Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has grea...
Generative model based compact video compression is typically operated within a relative narrow range of bitrates, and often with an emphasis on ult...
We propose Step-by-Step Coding (SBSC): a multi-turn math reasoning framework that enables Large Language Models (LLMs) to generate sequence of progr...
In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naiv...
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, es...
DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned represe...
In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the la...
The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. W...
This paper introduces rateless joint source-channel coding (rateless JSCC). The code is rateless in that it is designed and optimized for a continuu...
Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. ...
Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynam...
The coronavirus disease 2019 (COVID-19) pandemic necessitated a shift in healthcare delivery, emphasizing the need for remote patient monitoring (RPM)...
BACKGROUND: International Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have subst...
Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language mode...
Lossy image coding is the art of computing that is principally bounded by the image's rate-distortion function. This bound, though never accurately ...
Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstabl...
In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated...