Specifically, to fully explore the characteristics of the spectral and spatial contexts in HSIs, we propose a novel superpixel and pixel coclustering framework with bipartite graph partitioning in the ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

The rapid identification of respiratory virus outbreaks is needed to enable rational and effective public health interventions. We developed new quantitative approaches for simultaneous ...

The Center for Transyouth Health and Development at Children’s Hospital Los Angeles will close its doors July 22.

In its official statement, the agency noted, “The National Testing Agency (NTA) is in receipt of various representations from ...

Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

Self-supervised Representation Learning for Neuronal Morphologies This repository contains code to the paper Self-Supervised Graph Representation Learning for Neuronal Morphologies by M.A. Weis, L.