Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Automated Anomaly Detection in Unlabeled Computed Tomography Images
Author(s)	Donald Loveland, Hyojin Kim, T. Yong-Jin Han
On-Site Speaker (Planned)	Donald Loveland
Abstract Scope	X-ray computed tomography (CT) offers a non-destructive characterization method to analyze 3-dimensional structures. However, this process creates an abundance of unlabeled data that can be difficult to efficiently analyze. With advances in machine learning (ML), automated analysis has become an important area of research to expedite this process. That said, a common vulnerability of ML models come from anomalies that may create inaccurate predictions during inference time. In CT data for granular composite materials, anomalies can exist, such as vacancies, non-material object inclusions, and inconsistent contrasting, which prove difficult for trained scientists to identify, let alone ML methods. Our work explores clustering and pixel-wise signal processing to capture anomalies, finding that signal processing approaches perform better, especially in highly homogeneous cases. We also show that this technique is capable of providing clear visual feedback with a means of accurately quantifying anomalies within a given sample.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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