Accurate identification of melanoma is key to improving prognosis, while traditional dermatoscopic recognition suffers from subjectivity and poor consistency. This study aimed to develop a deep ...

This repository provides code and workflows to test several state-of-the-art vehicle detection deep learning algorithms —including YOLOX, SalsaNext, and RandLA-Net— on a Flash Lidar dataset. The ...

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to patient outcomes using widely available bulk RNA sequencing data. The approach ...

A research team co-led by scientists at the Netherlands Cancer Institute (NKI) and Oncode Institute has developed a deep learning model, PARM (promoter activity regulatory model) that offers up new ...

A deep learning model using baseline fundus images accurately predicted myopia and high myopia risk in school-aged children More than half of children without myopia at baseline developed the ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Abstract: This study explores the application and practice of deep learning algorithms and MATLAB software in teaching higher mathematics. The teaching case design of function approximation, ...

A deep learning model accurately identified mitral valve prolapse (MVP) from transthoracic echocardiograms (TTE), and its predictions were associated with clinical endpoints such as mitral ...

Predicting the effects of multiple mutations on protein function is challenging due to the intricate interplay between residues. Machine learning has advanced these efforts, but traditional neural ...

Abstract: In smart industries, cyber-attacks are the most consequential threats that can harm networks. Preventing cyberattacks from interrupting services is vital and challenging. Recently, ...