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, ...

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 ...

Abstract: Semantic segmentation is one of the fundamental tasks in understanding high-resolution aerial images. Recently, convolutional neural network (CNN) and fully convolutional network (FCN) have ...

Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their ...

Researchers at the UCLA Samueli School of Engineering and CNSI (California NanoSystems Institute), led by Professor Aydogan ...

Explore how AI phenotypic screening transforms image-based drug discovery through advanced phenotypic data analysis and ...

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...

At BIT Mesra in Ranchi, a three-woman team has trained AI to detect and analyse lunar craters. The ISRO-backed work could ...

For the Tulalip Tribes in Washington state, the wetlands nestled in the tribe’s forests and coasts are far from humble swamps and simple ponds. They’re vital for climate resilience and biodiversity — ...

This repository contains an implementation of SynthSR, a convolutional neural network that turns a clinical MRI scan (or even CT scan!) of any orientation, resolution and contrast into 1 mm isotropic ...

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, ...