Engineering researchers at the University of California, Los Angeles (UCLA) have developed an advanced ...

Deep learning high-content imaging is rapidly reshaping image-based screening in the modern laboratory environment. As high-content screening (HCS) generates increasingly large and complex datasets, ...

A new explainable deep learning framework could help greenhouse operators forecast crop yields and energy use more accurately while showing which environmental factors drive those predictions, ...

Abstract: We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we ...

1 Guangzhou Railway Polytechnic, Guangzhou, China. 2 School of Intelligent Construction and Civil Engineering, Zhongyuan University of Technology, Zhengzhou, China. 3 Research Center for Wind ...

Correspondence to Dr Sharon Fekrat, Department of Ophthalmology, Duke University, Durham, NC 27708-0187, USA; sharon.fekrat{at}duke.edu Background/Aims To develop a convolutional neural network (CNN) ...

This is a tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) ...

Researchers from Science Tokyo develop a Multi-scale Hessian-enhanced Patch-based Neural Network Model for Segmentation of Liver Tumor from CT Scans. Liver cancer is the sixth most common cancer ...

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Computer Vision (CV) has evolved rapidly ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Spectroscopic methods─like nuclear magnetic resonance, mass spectrometry, X-ray ...