Geriatrics

A Computer Vision Hybrid Approach: CNN and Transformer Models for Accurate Alzheimer's Detection from Brain MRI Scans

Early and accurate classification of Alzheimers disease (AD) from brain MRI scans is essential for timely clinical intervention and improved patient outcomes. This study presents a comprehensive comparative analysis of five CNN architectures (EfficientNetB0, ResNet50, DenseNet201, MobileNetV3, VGG16), five Transformer-based models (ViT, ConvTransformer, PatchTransformer, MLP-Mixer, SimpleTransform...

LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR

We present \textbf{LightOnOCR-2-1B}, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, naturally ordered text without brittle OCR pipelines. Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench whi...

Normal Breast Tissue (NBT)-Classifiers: Advancing Compartment Classification in Normal Breast Histology

Background: Cancer research emphasises early detection, yet quantitative methods for analysing normal tissue remain limited. Hematoxylin and eosin (H&...

ARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification

Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of...

Graph Neural Network Reveals the Local Cortical Morphology of Brain Aging in Normal Cognition and Alzheimers Disease

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approach to map the anatomic features of brain senescenc...

Generative Scenario Rollouts for End-to-End Autonomous Driving

Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works...

Vision-Language Models vs Autonomous AI Agents for Anterior Capsular Radial Folds: A Diagnostic Study

ImportanceVision-language models (VLMs) enable generalist multimodal reasoning, but their ability to resolve brief, low-contrast cues in surgical vide...

Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy

The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological p...

AI Agents Need Memory Control Over More Context

AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often deg...

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data

Aging is often accompanied by cognitive decline, but the extent, timing, and severity of this process is subject to large inter-individual variability...

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort

BackgroundThe ATN (Amyloid/Tau/Neurodegeneration) framework provides a theory-driven approach to Alzheimers disease (AD) classification using binary b...

Histological aging signatures enable tissue-specific disease prediction from blood

Aging, the leading risk factor for numerous diseases, manifests through diverse structural and architectural changes in human tissues, providing an op...

Towards Open Environments and Instructions: General Vision-Language Navigation via Fast-Slow Interactive Reasoning

Vision-Language Navigation aims to enable agents to navigate to a target location based on language instructions. Traditional VLN often follows a clos...

LP-LLM: End-to-End Real-World Degraded License Plate Text Recognition via Large Multimodal Models

Real-world License Plate Recognition (LPR) faces significant challenges from severe degradations such as motion blur, low resolution, and complex illu...

Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying diseas...

BegoniaGPT: Cultivating the large language model to be an exceptional K-12 English teacher.

Large language models (LLMs) have taken the natural language processing (NLP) domain by storm, and their transformative momentum has surged into the d...

Transcriptome analysis and machine learning methods reveal potential mechanisms of zebrafish muscle aging.

Muscle is one of the most abundant tissues in the human body, and its aging usually leads to many adverse consequences. Zebrafish is a powerful model ...

FedPPD: Towards effective subgraph federated learning via pseudo prototype distillation.

Subgraph federated learning (subgraph-FL) is a distributed machine learning paradigm enabling cross-client collaborative training of graph neural netw...

Temporal evolution stages classification and aging time prediction of gel-pen ink using GC-IMS combined with machine learning for forensic science applications.

Determining the temporal evolution of inks remains a critical challenge in forensic document analysis. The temporal evolution stages classification an...

A robust automated segmentation method for white matter hyperintensity of vascular-origin.

White matter hyperintensity (WMH) is a primary manifestation of small vessel disease (SVD), leading to vascular cognitive impairment and other disorde...