78 citations · 171 across the 8 of their papers we have counts for
4 papers · 1 filter
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
Ryan Jacobs, Mingren Shen, Yuhan Liu +10
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional…
Multi defect detection and analysis of electron microscopy images with deep learning
Mingren Shen, Guanzhao Li, Dongxia Wu +12
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalabl…
A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
Mingren Shen, Guanzhao Li, Dongxia Wu +4
Videos captured using Transmission Electron Microscopy (TEM) can encode details regarding the morphological and temporal evolution of a material by taking snapshots of the microstr…
Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
Nick Lawrence, Mingren Shen, Ruiqi Yin +2
The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation…