BACKGROUND Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). NEW METHOD T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transform...

Cyclic nigerosyl nigerose (CNN) is a cyclic tetrasaccharide that exhibits properties distinct from other conventional cyclodextrins. Herein, we demonstrate that treatment of B16 melanoma with CNN results in a dose-dependent decrease in melanin synthesis, even under conditions that stimulate melanin synthesis, without significant cytotoxity. The effects of CNN were prolonged for more than 27 day...

Convolutional Neural Network (CNN) was firstly introduced in Computer Vision for image recognition by LeCun et al. in 1989. Since then, it has been widely used in image recognition and classification tasks. The recent impressive success of Krizhevsky et al. in ILSVRC 2012 competition demonstrates the significant advance of modern deep CNN on image classification task. Inspired by his work, many...

This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional neural network (called pixel-level CNN) is constructed by modifying the VGGNet architecture to perform multiscale feature learning, based on which an image-to-image predict...

In this work, we study the realization and bifurcation of Boolean functions of four variables via a Cellular Neural Network (CNN). We characterize the basic relations between the genes and the offsets of an uncoupled CNN as well as the basis of the binary input vectors set. Based on the analysis, we have rigorously proved that there are exactly 1882 linearly separable Boolean functions of four ...

Cellular Neural Networks (CNN) [1] were introduced by Chua and Yang in 1988. The idea of the CNN was inspired from the architecture of the cellular automata and the neural networks. Unlike the conventional neural networks, the CNN has local connectivity property. Since the structure of the CNN resembles the structure of animals’ retina, the CNN can be used for various image processing applicati...

BACKGROUND AND AIMS The role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection. METHODS A 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. p...

The Drosophila Centrosomin (Cnn) protein is an essential core component of centrosomes in the early embryo. We have expressed a Cnn-GFP fusion construct in cleavage stage embryos, which rescues the maternal effect lethality of cnn mutant animals. The localization patterns seen with GFP-Cnn are identical to the patterns we see by immunofluorescent staining with anti-Cnn antibodies. Live imaging ...

A prolific nucleator program n studies of Drosophila cells, aurora-A kinase and centrosomin (CNN) are sometimes used as markers for centrosomes, but, other than their localization, relatively little is known about these proteins. On page 757, Terada et al. show that aurora-A and CNN must physically interact to be translocated to the centro-some, and, once there, CNN acts as a powerful microtubu...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...