Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …

There have been major developments in deep learning in computer vision since the 2010s.
Deep learning has contributed to a wealth of data in medical image processing, and …

Background and objective Automatic segmentation and annotation of medical image plays a
critical role in scientific research and the medical care community. Automatic segmentation …

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the
world. Consequently, the early detection of liver cancer leads to lower mortality rates. This …

This study aimed to propose a neural network (NN)-based method to evaluate thyroid-
associated orbitopathy (TAO) patient activity using orbital computed tomography (CT) …

Recent advancements in biomedical image analysis have been significantly driven by the
Segment Anything Model (SAM). This transformative technology, originally developed for …

Background Ultrasound imaging has been recognized as a powerful tool in clinical
diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of …

Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early
identification of cirrhotic patients with at a high risk of EV bleeding is key to improving …

Computed Tomography (CT) is one of the biomedical imaging modalities which are used to
confirm COVID-19 cases and/or to identify infected areas in the lung. Therefore, this article …

Image segmentation is a crucial aspect of clinical decision making in medicine, and as such,
it has greatly enhanced the sustainability of medical care. Consequently, biomedical image …