SY Huang, WL Hsu, RJ Hsu, DW Liu - Diagnostics, 2022 - mdpi.com
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 …
D Pal, PB Reddy, S Roy - Computers in Biology and Medicine, 2022 - Elsevier
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 …
J Lee, S Lee, WJ Lee, NJ Moon, JK Lee - Scientific Reports, 2023 - nature.com
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 …
Y Wang, Y Hong, Y Wang, X Zhou, X Gao, C Yu… - Journal of Digital …, 2023 - Springer
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 …