Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading ...
Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery o...
Over the past decades, air pollution has caused severe environmental and public health problems. Acc...
The increasing availability of biomedical data creates valuable resources for developing new deep le...
To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on...
BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels ar...
Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, he...
RATIONALE AND OBJECTIVES: The process of generating radiology reports is often time-consuming and la...
BACKGROUND: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring whe...
OBJECTIVES: The interpretation of mammograms requires many years of training and experience. Current...
Pre-training deep learning models with large data sets of natural images, such as ImageNet, has beco...
Domain Generalization (DG) focuses on the Out-Of-Distribution (OOD) generalization, which is able to...
Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, wh...
BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches prima...
BACKGROUND AND PURPOSE: Tumor segmentation is essential in surgical and treatment planning and respo...
BACKGROUND: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an i...
The recently introduced neural operator (NO) has been employed as a gain approximator in the backste...
BACKGROUND: Superior surgical skill improves surgical outcomes in endoscopic pituitary adenoma surge...
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models acros...
Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machi...
In recent decades, the rapid advances in information technology have promoted a widespread deploymen...