PET and MRI image fusion based on a dense convolutional network with dual attention.

The fusion techniques of different modalities in medical images, e.g., Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), are increasingly significant in many clinical applications by integrating the complementary information from different medical images.

Lightweight and efficient neural network with SPSA attention for wheat ear detection.

Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy.

SeedSortNet: a rapid and highly effificient lightweight CNN based on visual attention for seed sorting.

Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting.

Electrically Conducting Hydrogel Graphene Nanocomposite Biofibers for Biomedical Applications.

Conductive biomaterials have recently gained much attention, specifically owing to their application for electrical stimulation of electrically excitable cells. Herein, flexible, electrically conducting, robust fibers composed of both an alginate biopolymer and graphene components have been produced using a wet-spinning process. These nanocomposite fibers showed better mechanical, electrical, and electrochemical properties than did single fibers that were made solely from alginate.

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions.

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation.