Latest AI and machine learning research in geriatrics for healthcare professionals.
Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, ...
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 language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReS...
Accurate prediction of drug metabolites and enzyme selectivity is essential for rational drug design and safety assessment. However, existing computat...
Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2...
Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fideli...
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep lear...
Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck...
Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classif...
Disentangling pathological changes from physiological aging in 3D medical shapes is crucial for developing interpretable biomarkers and patient strati...
This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the ...
While aging manifests differently across organs and individuals, existing approaches to measure it lack the spatial resolution to capture this complex...
In aging-in-place contexts, small difficulties in Activities of Daily Living (ADL) can accumulate, affecting well-being through fatigue, anxiety, redu...
Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods...
Scene Text Image Super-Resolution (STISR) aims to restore high-resolution details in low-resolution text images, which is crucial for both human reada...
Existing text-to-video retrieval benchmarks are dominated by real-world footage where much of the semantics can be inferred from a single frame, leavi...
Percentage Brain Volume Change (PBVC) derived from Magnetic Resonance Imaging (MRI) is a widely used biomarker of brain atrophy, with SIENA among the ...
Collecting multiple types of data on the same set of subjects is common in modern scientific applications including, genomics, metabolomics, and neuro...
Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide associati...
Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, curre...
The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening to...