Latest AI and machine learning research in pain management for healthcare professionals.
Computed Tomography (CT) is essential for diagnosing pediatric craniofacial abnormalities, yet poses radiation risks to developing anatomies. Reconstructing 3D CT from sparse bi-planar X-rays offers a low-dose alternative but is severely ill-posed. Existing methods employ geometry-agnostic feature lifting, naively projecting 2D features into 3D without explicit spatial modeling, causing depth ambi...
Spiking neural networks (SNNs) have garnered significant interest in computer vision due to their potential for efficiency and biological inspiration. While spiking CNN-based methods have shown promise for image restoration (IR) tasks, their performance is constrained by the inherent receptive field limitations of CNN operations. In the paper, we explore the benefits of discrete wavelet transforma...
Text-guided Open-vocabulary Object Counting (TOOC) aims to estimate the number of objects described by text prompts, which is particularly challenging...
Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and...
Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis. However, acquiring all MRI sequences is oft...
Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance. The InferBERT framework ...
Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditionin...
The transition toward software-defined vehicles concentrates an increasing share of vehicle functionality into distributed software services, where fa...
A key challenge in multimodal reasoning is determining which visual dependencies become relevant under a specific task, rather than merely recognizing...
Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle co...
Spinal pathology is a leading cause of pain and disability worldwide. Spine magnetic resonance imaging (MRI) is central to clinical evaluation, yet it...
Post-surgical adverse outcomes, including mortality, intensive care readmission, and complications, remain major challenges for clinical decision-maki...
Background: This study aimed to evaluate real-world adverse event (AE) signals of EV to provide evidence-based guidance for its safe clinical applicat...
Background Machine learning (ML) models are increasingly used to predict adverse outcomes after surgery. However, most rely on static patient characte...
Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains comp...
Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challeng...
Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By alignin...
Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, ...
Introduction: Postmortem imaging at ultrahigh field strengths, such as 7 Tesla (7T) magnetic resonance imaging (MRI), enables unprecedented visualizat...
Title and abstract screening limit the timeliness of systematic reviews used for clinical guidelines. We evaluated audited large language model (LLM) ...