In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis
on image data. However, one critical problem is that Deep Learning comes with …

The application of deep learning techniques to analyze brain functional magnetic resonance
imaging (fMRI) data has led to significant advancements in identifying prospective …

The non-invasive and low radiation exposure methodology of brain computed tomography
(CT) useful for effective diagnosis of brain lesions, such as brain hemorrhage. Misdiagnosis …

The quality of patient care associated with diagnostic radiology is proportionate to a
physician's workload. Segmentation is a fundamental limiting precursor to both diagnostic …

Compared to standard machine learning models, deep learning models are largely superior
at discerning patterns and discriminative features in brain imaging, despite being more …

Analyzing functional brain networks (FBN) with deep learning has demonstrated great
potential for brain disorder diagnosis. The conventional construction of FBN is typically …

The increasing complexity and availability of neuroimaging data, computational resources,
and algorithms have the potential to exponentially accelerate discoveries in the field of …

Objective: To perform ultrasound image segmentation of the median nerve in the forearm
and the wrist using deep learning algorithms. Background: The recent advancement of …

There is an emerging trend to employ dynamic sonography in the diagnosis of entrapment
neuropathy, which exhibits aberrant spatiotemporal characteristics of the entrapped nerve …

在現今社會, 已有許多老年人換上阿兹海默症(Alzheimer's Disease, AD). 阿兹海默症是一種
不可逆的神經退化性疾病, 會造成老年人喪失記憶, 語言障礙, 冷漠等問題 …