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

• URL: http://arxiv.org/abs/2101.08449v2
• Date: Fri, 22 Jan 2021 01:58:29 GMT
• Title: Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy
• Authors: Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, and Maxim Ziatdinov
• Abstract summary: Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines. Here we explore the application of deep learning for feature extraction in atom-resolved electron microscopy. This approach both allows uncertainty into deep learning analysis and also enables automated experimental detection where retraining of network to compensate for out-of-distribution drift due to change in imaging conditions is substituted for a human operator or programmatic selection of networks from the ensemble.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments [0.22795086293129713]
  Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation. Here, we operationalize and benchmark a recently proposed reward-driven optimization workflow for on-the fly image analysis in STEM. This unsupervised approach is much more robust, as it does not rely on human labels and is fully explainable.
  arXiv  Detail & Related papers  (2024-09-19T04:51:13Z)
• Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy [55.2480439325792]
  We introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder. Our proposed method exceeds the performance of its supervised counterparts.
  arXiv  Detail & Related papers  (2024-03-25T17:40:32Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data" [0.0]
  In-situ TEM experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed.
  arXiv  Detail & Related papers  (2023-07-12T17:37:46Z)
• Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization [49.62449457005743]
  We develop a flexible deep learning library for histopathology called Slideflow. It supports a broad array of deep learning methods for digital pathology. It includes a fast whole-slide interface for deploying trained models.
  arXiv  Detail & Related papers  (2023-04-09T02:49:36Z)
• Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning [0.0]
  Key tasks in experimental studies is establishing which of these channels is predictive of the behaviors of interest. Here we explore the problem of discovery of the optimal predictive channel for structure-property relationships in microscopy. This approach can be directly applicable to similar active learning tasks in automated synthesis and the discovery of quantitative structure-activity relations in molecular systems.
  arXiv  Detail & Related papers  (2022-03-18T22:36:40Z)
• Causal Navigation by Continuous-time Neural Networks [108.84958284162857]
  We propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks.
  arXiv  Detail & Related papers  (2021-06-15T17:45:32Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Learning Numerical Observers using Unsupervised Domain Adaptation [13.548174682737756]
  Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors.
  arXiv  Detail & Related papers  (2020-02-03T22:58:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.