Nature

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models ...
Nature

Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke

According to C. Tsao, et al., 2023, and Hou, Shuai, et al., 2021, stroke is the second most frequent cause of death globally, characterized by its devastating impact on brain health. During a stroke, ...
EurekAlert!

A novel deep learning model for medical image segmentation with convolutional neural network and transformer

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...