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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Data Science and Analytics for Materials Imaging and Quantification
Presentation Title Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Author(s) Bo Lei, Elizabeth Holm
On-Site Speaker (Planned) Bo Lei
Abstract Scope In quantitative microscopy, microstructure image segmentation is essential for image analysis and materials characterization. The rising deep convolutional neural network methods for semantic segmentation in natural images have recently transferred to materials images and demonstrated outstanding performance in complicated microstructure datasets. However, current supervised learning solutions require pixel-level human annotations, which is painstaking, biased and infeasible for some cases. It is worth exploring the possibility to go from fully supervised to semi-supervised and unsupervised methods with the goal of achieving comparable performance while alleviating the annotation cost. Here, we demonstrate our effort in moving from supervised methods to unsupervised methods. Multiple aspects of the strategies including dataset generation, annotation, transfer learning, evaluation, etc., are discussed.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancements in EBSD Using Machine Learning
Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Deep Neural Network Facilitated Complex Imaging of Phase Domains
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions
Improved EBSD Indexing through Non-Local Pattern Averaging
Materials Characterization in 3D Using High Energy X-ray Diffraction Microscopy: Irradiated and Deformed Materials
Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Quantitative EBSD Image Analysis and Prediction via Deep Learning
Quantitative X-ray Fluorescence Nanotomography
Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Understanding the Keyhole Dynamics in Laser Processing Using Time-resolved X-ray Imaging Coupled With Computer Vision and Data Analytics

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