Papers Alzheimer's Disease Detection
“Alzheimer's Disease Detection” 태그가 달린 논문 78편 · 필터 해제
Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection
Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography (EEG) is portable and inexpensive, but its recordings are noisy, vary…
Alzheimer's Disease DetectionContrastive LearningDecoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
Brain parcellation and classification are typically evaluated in isolation, yet downstream AD detection performance depends on their interaction. We decouple these components and systematically benchmark fast deep learni…
Alzheimer's Disease DetectionBrain SegmentationGated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection
Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi-View …
Alzheimer's Disease DetectionSpeech RecognitionNeuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection
Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD) and often precede cognitive decline. NPS assessments hold promise as early detection markers due to their correlation w…
Alzheimer's Disease DetectionImpact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation
Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical langu…
Alzheimer's Disease DetectionSpeech RecognitionBreaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech
Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI-based speech detection is non-invasive and cost-effective, it faces a critical data efficiency dilemma due to medical data sca…
Alzheimer's Disease DetectionFederated LearningData AugmentationVoice ConversionDiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection
Early diagnosis of Alzheimer's disease (AD) remains a major challenge due to the subtle and temporally irregular progression of structural brain changes in the prodromal stages. Existing deep learning approaches require …
Alzheimer's Disease DetectionMethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread epigenetic dysregulation in the brain. DNA methylation, as a stable yet dynamic epigen…
Alzheimer's Disease DetectionBeyond surface form: A pipeline for semantic analysis in Alzheimer's Disease detection from spontaneous speech
Alzheimer's Disease (AD) is a progressive neurodegenerative condition that adversely affects cognitive abilities. Language-related changes can be automatically identified through the analysis of outputs from linguistic a…
Alzheimer's Disease DetectionSemantic SimilarityUG-FedDA: Uncertainty-Guided Federated Domain Adaptation for Multi-Center Alzheimer's Disease Detection
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder, and early diagnosis is critical for timely intervention. However, most existing classification frameworks face challenges in multicenter studies, as…
Alzheimer's Disease DetectionDomain AdaptationBeyond Plain Demos: A Demo-centric Anchoring Paradigm for In-Context Learning in Alzheimer's Disease Detection
Detecting Alzheimer's disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript demos describe the same scene, producing…
Alzheimer's Disease DetectionEarly Alzheimer's Disease Detection from Retinal OCT Images: A UK Biobank Study
Alterations in retinal layer thickness, measurable using Optical Coherence Tomography (OCT), have been associated with neurodegenerative diseases such as Alzheimer's disease (AD). While previous studies have mainly focus…
Alzheimer's Disease DetectionMedical Image ClassificationBRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring
As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS…
Alzheimer's Disease DetectionWhen Deep Learning Fails: Limitations of Recurrent Models on Stroke-Based Handwriting for Alzheimer's Disease Detection
Alzheimer's disease detection requires expensive neuroimaging or invasive procedures, limiting accessibility. This study explores whether deep learning can enable non-invasive Alzheimer's disease detection through handwr…
Alzheimer's Disease DetectionTemporal SequencesMeta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Developing effective multimodal data fusion strategies has become increasingly essential for improving the predictive power of statistical machine learning methods across a wide range of applications, from autonomous dri…
Alzheimer's Disease DetectionAutonomous DrivingEnsemble LearningMedical DiagnosisExploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception
Gender bias has been widely observed in speech perception tasks, influenced by the fundamental voicing differences between genders. This study reveals a gender bias in the perception of Alzheimer's Disease (AD) speech. I…
Alzheimer's Disease DetectionAn Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide. In the absence of effective treatment options, early diagnosis is crucial for initiating management strategies to delay disease on…
Alzheimer's Disease DetectionFeature ImportanceDelta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzing linguistic abnormalities. In this work, we explore the potential of L…
Alzheimer's Disease DetectionIn-Context LearningSingle Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning
Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder …
Alzheimer's DetectionAlzheimer's Disease DetectionContrastive LearningDiversity+1Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection
Recent breakthroughs in Automatic Speech Recognition (ASR) have enabled fully automated Alzheimer's Disease (AD) detection using ASR transcripts. Nonetheless, the impact of ASR errors on AD detection remains poorly under…
Alzheimer's Disease DetectionAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+1