Geriatrics

End-to-End Action Segmentation Transformer

Existing approaches to action segmentation use pre-computed frame features extracted by methods which have been trained on tasks that are different from action segmentation. Also, recent approaches typically use deep framewise representations that lack explicit modeling of action segments. To address these shortcomings, we introduce the first end-to-end solution to action segmentation -- End-to-...

TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking

In recent years, fully differentiable end-to-end autonomous driving systems have become a research hotspot in the field of intelligent transportation. Among various research directions, automatic parking is particularly critical as it aims to enable precise vehicle parking in complex environments. In this paper, we present a purely vision-based transformer model for end-to-end automatic parking,...

MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model

Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortali...

An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization

Multi-sensor fusion is essential for autonomous vehicle localization, as it is capable of integrating data from various sources for enhanced accurac...

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes

Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diag...

Security and Real-time FPGA integration for Learned Image Compression

Learnable Image Compression (LIC) has proven capable of outperforming standardized video codecs in compression efficiency. However, achieving both r...

BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification

The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD a...

CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance

We address the long-horizon mapless navigation problem: enabling robots to traverse novel environments without relying on high-definition maps or pr...

DDCSR: A Novel End-to-End Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI

Diffusion MRI (dMRI) plays a crucial role in studying brain white matter connectivity. Cortical surface reconstruction (CSR), including the inner wh...

Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion

Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often ...

Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification

Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of priva...

Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structu...

MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments

Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipmen...

Disentangled Knowledge Tracing for Alleviating Cognitive Bias

In the realm of Intelligent Tutoring System (ITS), the accurate assessment of students' knowledge states through Knowledge Tracing (KT) is crucial f...

Are Large Vision Language Models Good Game Players?

Large Vision Language Models (LVLMs) have demonstrated remarkable abilities in understanding and reasoning about both visual and textual information...

CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computin...

Generative Model-Assisted Demosaicing for Cross-multispectral Cameras

As a crucial part of the spectral filter array (SFA)-based multispectral imaging process, spectral demosaicing has exploded with the proliferation o...

The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an op...

OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels

Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple i...

Modality-Agnostic Style Transfer for Holistic Feature Imputation

Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, ma...