Methods in molecular biology (Clifton, N.J.)
40220224
Fluorescence microscopy is a key method for the visualization of cellular, subcellular, and molecular live-cell dynamics, enabling access to novel insights into mechanisms of health and disease. However, effects like phototoxicity, the fugitive natur...
Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading t...
PURPOSE: This study aims to compare the effects of two types of deep learning (DL) techniques on brain CT values, image noise content, and contrast-to-noise ratio (CNR) between white and gray matter in low-noise head CT images, along with adaptive it...
Computer methods and programs in biomedicine
40168942
BACKGROUND AND OBJECTIVE: Computer-aided diagnosis systems based on deep neural networks heavily rely on datasets with high-quality labels. However, manual annotation for lesion diagnosis relies on image features, often requiring professional experie...
SIGNIFICANCE: High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely...
The natural habitats of wild horseshoe crabs (such as beaches, shallow water areas, and intertidal sediments) are complex, posing challenges for image capture, which is often affected by real noise factors. Deep learning models are widely used in ima...
Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by differ...
OBJECTIVE: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI) protocol with standard AMRI (AMRI) of the liver in terms of image quality and malignant focal lesion detection.