Latest AI and machine learning research in geriatrics for healthcare professionals.
Early diagnosis of Alzheimer's Disease (AD), especially at the mild cognitive impairment (MCI) stage, is vital yet hindered by subjective assessments and the high cost of multimodal imaging modalities. Although deep learning methods offer automated alternatives, their energy inefficiency and computational demands limit real-world deployment, particularly in resource-constrained settings. As a br...
We propose a feed-forward Gaussian Splatting model that unifies 3D scene and semantic field reconstruction. Combining 3D scenes with semantic fields facilitates the perception and understanding of the surrounding environment. However, key challenges include embedding semantics into 3D representations, achieving generalizable real-time reconstruction, and ensuring practical applicability by using...
Early detection is crucial for timely intervention aimed at preventing and slowing the progression of neurocognitive disorder (NCD), a common and si...
The continuous improvements on image compression with variational autoencoders have lead to learned codecs competitive with conventional approaches ...
The National Institute on Aging (NIA) is at the forefront of leveraging advances in artificial intelligence (AI) to better understanding of aging and ...
OBJECTIVES: Device-based sedentary time shows a nonlinear association with incident dementia among older adults. However, associations between sedenta...
BACKGROUND: As the global population ages healthcare challenges are escalating. Frailty, a clinical syndrome characterized by decreased reserve and re...
End-to-end autonomous driving has emerged as a dominant paradigm, yet its highly entangled black-box models pose significant challenges in terms of ...
The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual rea...
Accurate sleep stage classification is essential for diagnosing sleep disorders, particularly in aging populations. While traditional polysomnograph...
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key co...
A central challenge in modern language models (LMs) is intrinsic hallucination: the generation of information that is plausible but unsubstantiated ...
Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly ...
Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi...
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre...
Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in co...
Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's ...
This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across ...
Real-world applications like video gaming and virtual reality often demand the ability to model 3D scenes that users can explore along custom camera...
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, ...