Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

by   Ayana Ghosh, et al.

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach both allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for a human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.



There are no comments yet.


page 7

page 9

page 10

page 11

page 12

page 20

page 22


Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements

Uncertainty quantification by ensemble learning is explored in terms of ...

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

We present a comparison of methods for uncertainty quantification (UQ) i...

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structu...

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data

Classification of ultrasound (US) kidney images for diagnosis of congeni...

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

The high dynamics and heterogeneous interactions in the complicated urba...

Epileptic seizure detection using deep learning techniques: A Review

A variety of screening approaches have been proposed to diagnose epilept...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.