Latest AI and machine learning research in work force for healthcare professionals.
With the ongoing expansion of artificial intelligence data centers and the increasing demand for hig...
Satellite-to-ground laser communications can download massive amounts of data from satellites, thus ...
Noise is a key factor determining imaging quality for optical coherence tomography (OCT). Although d...
Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene...
Emerging spatial profiling technologies have revolutionized our understanding of how tissue architec...
BACKGROUND: Recent advancements in general multimodal large language models (MLLMs) have led to subs...
Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, a...
Medical information extraction, as a core task in medical intelligent systems, focuses on extracting...
Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While...
This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology i...
INTRODUCTION: A shortage of trained retinal specialists has created a growing need for a telehealth ...
Anesthesiology has a longstanding commitment to patient safety, characterized by innovative research...
Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effectiv...
IMPORTANCE: The US healthcare system faces significant challenges, including clinician burnout, oper...
Recently, post-training quantization (PTQ) has become the de facto way to produce efficient low-prec...
Foundation models are usually pre-trained on large-scale datasets and then adapted to different down...
Energy-based models (EBMs) show their efficiency in density estimation. However, MCMC sampling in tr...
The divergence between labeled training data and unlabeled testing data is a significant challenge f...
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination an...
Existing research on federated learning (FL) usually assumes that training labels are of high qualit...
Recently, quantum federated learning (QFL) has received significant attention as an innovative parad...