DeepAI AI Chat
Log In Sign Up

Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation

12/12/2019
by   James P. Horwath, et al.
0

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical parameters. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding e.g. the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required.

READ FULL TEXT

page 3

page 4

page 5

page 8

page 10

page 13

page 14

page 15

10/04/2022

Analysis of the performance of U-Net neural networks for the segmentation of living cells

The automated analysis of microscopy images is a challenge in the contex...
12/16/2016

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

Electron microscopic connectomics is an ambitious research direction wit...
11/13/2019

Cost-efficient segmentation of electron microscopy images using active learning

Over the last decade, electron microscopy has improved up to a point tha...
04/16/2019

Is deep learning a good choice for image segmentation?

Deep learning works as a discrete non-linear mapping function and has ac...
09/17/2020

Review: Deep Learning in Electron Microscopy

Deep learning is transforming most areas of science and technology, incl...
09/27/2020

A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time

Semantic image segmentation is one of fastest growing areas in computer ...