For decades, researchers have explored how electrons behave in quantum materials. Under certain conditions, electrons ...

A deep learning model and training/testing/inference library in PyTorch for segmentation, classification, object detection and self-supervised learning using radiology data.

Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States Neuroscience Interdepartmental Program, University of California, Los ...

A spinal MRI, or magnetic resonance imaging ... The presence of medical devices can also affect the quality of the MRI image. Your doctor will check beforehand to see if and what type of ...

Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging ... relevance and utility of these atlases. Structural ...

These magnetars have magnetic dipoles that are ten to one hundred times weaker than those of classical magnetars. This study therefore demonstrates that these magnetars are probably formed in neutron ...

Animal Navigation, Institute of Biology and Environmental Sciences, School of Mathematics and Science, Carl von Ossietzky Universität Oldenburg, Oldenburg D-26111, Germany Animal Navigation, Institute ...

We introduce a large image dataset HaGRIDv2 (HAnd Gesture Recognition Image Dataset) for hand gesture recognition (HGR) systems. You can use it for image classification or image detection tasks.

Clinically, magnetic resonance imaging (MRI ... aims to classify data that have not been assigned labels or categories; examples include neural networks and clustering to map input data (e.g., breast ...