Latest AI and machine learning research in geriatrics for healthcare professionals.
Integer quantization has emerged as a critical technique to facilitate deployment on resource-constrained devices. Although they do reduce the complexity of the learning models, their inference performance is often prone to quantization-induced errors. To this end, we introduce InTreeger: an end-to-end framework that takes a training dataset as input, and outputs an architecture-agnostic integer...
In endoscopic procedures, autonomous tracking of abnormal regions and following circumferential cutting markers can significantly reduce the cognitive burden on endoscopists. However, conventional model-based pipelines are fragile for each component (e.g., detection, motion planning) requires manual tuning and struggles to incorporate high-level endoscopic intent, leading to poor generalization ...
End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error ac...
BACKGROUND: Movement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using we...
INTRODUCTION: Injuries induced by falls represent the main cause of failure in the French Navy Special Forces selection course. In the present study, ...
Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the leading cause of dementia worldwide and remains incurable once it begins. T...
Many practical medical imaging scenarios include categories that are under-represented but still crucial. The relevance of image recognition models ...
Large Vision-Language Models (VLMs) have shown strong capabilities in multimodal understanding and reasoning, yet they are primarily constrained by ...
Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) t...
This paper introduces a comprehensive end-to-end pipeline for Optical Character Recognition (OCR) on Urdu newspapers. In our approach, we address th...
A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating he...
Autoregressive (AR) models have recently shown strong performance in image generation, where a critical component is the visual tokenizer (VT) that ...
Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first s...
Learning Analytics Dashboards (LADs) often fall short of their potential to empower learners, frequently prioritizing data visualization over the co...
For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficult...
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurat...
This paper introduces a novel dataset and evaluation benchmark designed to assess and improve small language models deployable on edge devices, with...
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are cap...
Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can gene...
Multimodal medical image segmentation faces significant challenges in the context of gastric cancer lesion analysis. This clinical context is define...