Abstract: Change detection (CD) aims to reveal the dynamic evolution of the Earth’s surface by analyzing multitemporal remote sensing imagery, exhibiting significant application value in critical ...

Purpose: This study evaluates the impact of high-resolution T2-weighted imaging (T2 HR) combined with deep learning image reconstruction (DLR) on image quality, lesion delineation, and extraprostatic ...

If high school is the runway, it should launch students onto the first on-ramp of long life learning, with multiple lanes leading to degrees, industry credentials, and real paychecks. For a long time, ...

This study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By ...

Abstract: In this research, we present a comprehensive comparative analysis of multiple state-of-the-art deep learning models for single image super-resolution (SISR). The models investigated include ...

Deep learning–based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low–field strength magnetic resonance imaging (LF-MRI).

The authors use deep mutational scanning to assess the effect of ~6,600 protein-coding variants in MC4R, a G-protein-coupled receptor associated with obesity. They develop new, more precise approaches ...

The increasing use of tissue clearing techniques underscores the urgent need for cost-effective and simplified deep imaging methods. While traditional inverted confocal microscopes excel in ...