OBJECTIVES: Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated dete...
OBJECTIVES: This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features.
We present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Our setup is particularly simple and robust, since it does not include any moving parts and is compati...
Endoscopic photoacoustic tomography (EPAT) is a catheter-based hybrid imaging modality capable of providing structural and functional information of biological luminal structures, such as coronary arterial vessels and the digestive tract. The recover...
OBJECTIVE: Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for wh...
The limited-view issue can cause a low-quality image in photoacoustic (PA) computed tomography due to the limitation of geometric condition. The model-based method is used to resolve this problem, which contains different regularization. To adapt fas...
BACKGROUND: Intracranial hemorrhage (ICH) is considered an emergency that requires rapid medical or surgical management. Previous studies have used artificial intelligence to attempt to expedite the diagnosis of this pathology on neuroimaging. Howeve...
Mathematical biosciences and engineering : MBE
Mar 16, 2022
The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid te...
PURPOSE: Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to caus...
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