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Machine Learning for Assisting Atomic- Resolution Electron Microscopy
Matthew Helmi Leth Larsen
Computational Atomic-scale Materials Design
Department of Physics
Research output
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Book/Report
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Ph.D. thesis
588
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Dive into the research topics of 'Machine Learning for Assisting Atomic- Resolution Electron Microscopy'. Together they form a unique fingerprint.
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Weight
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Computer Science
Neural Network
100%
Machine Learning
100%
Learning System
100%
Neural Network Architecture
23%
U-Net
23%
Transfer Function
23%
Trained Neural Network
15%
Deep Learning Method
15%
Resolution Image
15%
Large Data Set
7%
Image Sequence
7%
Automated Analysis
7%
Large Scale Data
7%
Data Extraction
7%
Simulated Image
7%
Microscopy Data
7%
Network Segmentation
7%
Data Analysis
7%
watershed algorithm
7%
Graphical User Interface
7%
Neural Network Training
7%
Training Data
7%
Segmentation Map
7%
Dynamic Property
7%
Local Structure
7%
Data Processing
7%
Signal-to-Noise Ratio
7%
Material Science
Nanoparticle
100%
High-Resolution Transmission Electron Microscopy
100%
Electron Microscopy
100%
Monolayers
60%
Cerium Oxide
40%
Transmission Electron Microscopy
40%
Signal-to-Noise Ratio
20%
Materials Design
20%
Oxide Compound
20%
Metal Nanoparticle
20%
Physics
Neural Network
100%
Electron Microscopy
100%
Machine Learning
100%
Transmission Electron Microscopy
36%
High Resolution
31%
Nanoparticle
21%
Au Nanoparticles
10%
Electron Beam
10%
Electron Microscope
10%
Data Transmission
10%
Modulation Transfer Function
10%
Deep Learning Method
10%
High Energy Electron
5%
Dynamic Property
5%
Electron Counter
5%
Signal-to-Noise Ratio
5%
Transfer Function
5%
Graphical User Interface
5%
Crystal
5%
Oxide
5%
Keyphrases
Mask Segmentation
10%
Multivalued Mapping
10%