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 ...

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 ...

A team led by Raju Tomer, professor of biological sciences at Columbia University, has created a new design for microscopes ...

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

AIIA Lab, Harbin Institute of Technology. This repository is the official PyTorch implementation of "Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution". If our work helps your ...

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 ...

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 — ...