Nature

Frame points attention convolution for deep learning on point cloud

Point Cloud can be considered as non-Euclidean structure data since it is disordered and irregular. When training on point cloud, it is difficult to apply spatial discrete convolution directly. In ...
eLife

An image segmentation method based on the spatial correlation coefficient of Local Moran’s I

Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here we propose a spatial ...
C&EN

Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks

Molecular Dynamics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands ...
Nature

Fast and simple super-resolution with single images

There is a fundamental limit to the spatial resolution of optical microscopy, due to the diffraction of light. A sharp point source will appear smeared out and have the shape of the point spread ...
C&EN

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

University of Groningen, W. J. Kolff Institute for Biomedical Engineering and Materials Science, Department of Biomedical Engineering, University Medical Center Groningen, A. Deusinglaan 1, 9713 AV ...
Frontiers

Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism

Nuclei segmentation is a fundamental but challenging task in histopathological image analysis. One of the main problems is the existence of overlapping regions which increases the difficulty of ...
Frontiers

Brain Network Analysis and Classification Based on Convolutional Neural Network

Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not ...