Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders
A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and...
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01.08.2025
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| Abstract | A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L‐edge ΔE peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.
Automated anomaly detection is demonstrated for electron energy‐loss spectrum imaging in an atomic‐resolution scanning transmission electron microscope using an unsupervised learning approach. A 3D convolutional variational autoencoder is introduced and tested on the iron L‐edge spectra taken from a single‐crystal BiFeO3 sample. This approach is benchmarked against Principal Component Analysis and high reconstruction quality is demonstrated even in challenging, noise‐dominated spectral regions. |
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| AbstractList | A 3D Convolutional Variational Autoencoder (3D-CVAE) is introduced for automated anomaly detection in electron energy-loss spectroscopy spectrum imaging (EELS-SI) data. This approach leverages the full 3D structure of EELS-SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross-entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect-free material. In exploring methods for anomaly detection, both the 3D-CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L-edge ΔE peak shifts designed to simulate material defects. These results show that 3D-CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower-dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise-dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS-SI data, particularly valuable for analyzing complex material systems. A 3D Convolutional Variational Autoencoder (3D-CVAE) is introduced for automated anomaly detection in electron energy-loss spectroscopy spectrum imaging (EELS-SI) data. This approach leverages the full 3D structure of EELS-SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross-entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect-free material. In exploring methods for anomaly detection, both the 3D-CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L-edge ΔE peak shifts designed to simulate material defects. These results show that 3D-CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower-dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise-dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS-SI data, particularly valuable for analyzing complex material systems.A 3D Convolutional Variational Autoencoder (3D-CVAE) is introduced for automated anomaly detection in electron energy-loss spectroscopy spectrum imaging (EELS-SI) data. This approach leverages the full 3D structure of EELS-SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross-entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect-free material. In exploring methods for anomaly detection, both the 3D-CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L-edge ΔE peak shifts designed to simulate material defects. These results show that 3D-CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower-dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise-dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS-SI data, particularly valuable for analyzing complex material systems. A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L ‐edge Δ E peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems. A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L‐edge ΔE peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems. A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L‐edge ΔE peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems. Automated anomaly detection is demonstrated for electron energy‐loss spectrum imaging in an atomic‐resolution scanning transmission electron microscope using an unsupervised learning approach. A 3D convolutional variational autoencoder is introduced and tested on the iron L‐edge spectra taken from a single‐crystal BiFeO3 sample. This approach is benchmarked against Principal Component Analysis and high reconstruction quality is demonstrated even in challenging, noise‐dominated spectral regions. A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L‐edge ΔE peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems. Automated anomaly detection is demonstrated for electron energy‐loss spectrum imaging in an atomic‐resolution scanning transmission electron microscope using an unsupervised learning approach. A 3D convolutional variational autoencoder is introduced and tested on the iron L‐edge spectra taken from a single‐crystal BiFeO3 sample. This approach is benchmarked against Principal Component Analysis and high reconstruction quality is demonstrated even in challenging, noise‐dominated spectral regions. |
| Author | Ayyubi, R. A. W. Buban, James P. Klie, Robert F. Sultanov, Seyfal |
| AuthorAffiliation | 1 Department of Computer Science University of Illinois Chicago Chicago IL 60607 USA 2 Department of Physics University of Illinois Chicago Chicago IL 60607 USA |
| AuthorAffiliation_xml | – name: 2 Department of Physics University of Illinois Chicago Chicago IL 60607 USA – name: 1 Department of Computer Science University of Illinois Chicago Chicago IL 60607 USA |
| Author_xml | – sequence: 1 givenname: Seyfal orcidid: 0009-0005-4948-7811 surname: Sultanov fullname: Sultanov, Seyfal organization: University of Illinois Chicago – sequence: 2 givenname: R. A. W. surname: Ayyubi fullname: Ayyubi, R. A. W. organization: University of Illinois Chicago – sequence: 3 givenname: James P. surname: Buban fullname: Buban, James P. organization: University of Illinois Chicago – sequence: 4 givenname: Robert F. orcidid: 0000-0003-4773-6667 surname: Klie fullname: Klie, Robert F. email: rfklie@uic.edu organization: University of Illinois Chicago |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40619908$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1103/PhysRevB.108.214428 10.1098/rspl.1895.0041 10.1088/2632-2153/acf6a9 10.1016/0304-3991(89)90304-5 10.1103/PhysRevLett.99.047203 10.1016/0921-4534(93)90501-G 10.1016/j.apsadv.2023.100523 10.1021/nl1022139 10.1038/416826a 10.1007/978-1-4419-7200-2 10.1177/14759217211073301 10.1023/A:1021428003587 10.1021/acs.chemmater.3c02773 10.1186/s40679-018-0052-y 10.1088/2632-2153/ad073b 10.1038/s41598-021-97668-8 10.3389/fnins.2021.652987 10.1109/ACCESS.2018.2848210 10.1126/science.1127647 10.1016/j.cviu.2020.102920 10.1126/science.aaa8415 10.1109/TSMC.1979.4310076 10.1038/nature14539 10.1103/RevModPhys.91.045002 10.1093/mam/ozae044.180 10.1109/5.726791 10.1109/TGRS.2019.2908756 10.1038/s42256-022-00555-8 10.1103/PhysRevLett.92.095502 10.1002/smll.202205977 10.1038/nature03644 10.1109/ACCESS.2021.3065838 10.1038/336565a0 10.1063/1.1330572 |
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| Keywords | convolutional variational autoencoders spectral imaging anomaly detection spectral anomalies electron energy loss spectroscopy |
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| References | 2021; 9 2010; 10 2019; 91 1979; 79 2023; 4 2023; 18 2011 2015; 521 2019; 57 2005; 435 2023; 19 2024; 30 2002; 416 2015; 349 2023; 108 2024; 36 2006; 313 2007; 99 1998; 86 1989; 28 2018; 6 2021; 15 2004; 92 2023; 22 2021; 11 2018; 4 2020; 195 2022 2022; 4 2002; 84 2015; 114 1895; 58 2000; 77 2019 2018 2017 2015 1993; 212 1979; 9 1988; 336 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 Hui Y. (e_1_2_9_18_1) 2018 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 Paszke A. (e_1_2_9_40_1) 2019 e_1_2_9_36_1 e_1_2_9_16_1 Lin R. (e_1_2_9_17_1) 2021; 11 e_1_2_9_19_1 Kingma D. P. (e_1_2_9_21_1) 2022 Rombach R. (e_1_2_9_37_1) 2022 e_1_2_9_41_1 e_1_2_9_42_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 e_1_2_9_1_1 e_1_2_9_9_1 e_1_2_9_26_1 Egerton R. (e_1_2_9_6_1) e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 Li M. D. (e_1_2_9_11_1) 2015; 114 |
| References_xml | – year: 2011 – volume: 91 year: 2019 publication-title: Rev. Mod. Phys. – volume: 416 start-page: 826 year: 2002 publication-title: Nature – volume: 30 year: 2024 publication-title: Microsc. Microanal. – volume: 336 start-page: 565 year: 1988 publication-title: Nature – volume: 195 year: 2020 publication-title: Comput. Vision Image Understanding – volume: 84 start-page: 193 year: 2002 publication-title: Catal. Lett. – volume: 11 year: 2021 publication-title: Sci. Rep. – year: 2019 publication-title: Pytorch: An imperative style, high‐performance deep learning library – volume: 28 start-page: 252 year: 1989 publication-title: Ultramicroscopy – volume: 6 year: 2018 publication-title: IEEE Access – volume: 77 start-page: 3737 year: 2000 publication-title: Appl. Phys. Lett. – year: 2018 publication-title: arXiv – volume: 18 year: 2023 publication-title: Appl. Surf. Sci. Adv. – volume: 521 start-page: 436 year: 2015 publication-title: Nature – volume: 4 start-page: 1101 year: 2022 publication-title: Nat. Mach. Intell. – volume: 15 year: 2021 publication-title: Front. Neurosci. – volume: 99 year: 2007 publication-title: Phys. Rev. Lett. – volume: 114 year: 2015 publication-title: Phys. Rev. Lett. – volume: 58 start-page: 240 year: 1895 publication-title: Proc. R. Soc. Lond. – volume: 22 start-page: 39 year: 2023 publication-title: Struct. Health Monit. – volume: 79 start-page: 1 year: 1979 end-page: 14 – volume: 4 year: 2023 publication-title: Mach. Learn.: Sci. Technol. – volume: 57 start-page: 6808 year: 2019 publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 4 year: 2023 publication-title: Machine Learning: Science and Technology – volume: 92 year: 2004 publication-title: Phys. Rev. Lett. – volume: 108 year: 2023 publication-title: Phys. Rev. B – volume: 313 start-page: 504 year: 2006 publication-title: Science – volume: 349 start-page: 255 year: 2015 publication-title: Science – volume: 19 year: 2023 publication-title: Small – year: 2017 – volume: 435 start-page: 475 year: 2005 publication-title: Nature – volume: 4 year: 2018 publication-title: Adv. Struct. Chem. Imaging – volume: 9 start-page: 62 year: 1979 publication-title: IEEE Trans. Syst. Man Cybern. – volume: 36 start-page: 2743 year: 2024 publication-title: Chem. Mater. – volume: 10 start-page: 3209 year: 2010 publication-title: Nano Lett. – year: 2022 publication-title: arXiv – year: 2015 – volume: 212 start-page: 185 year: 1993 publication-title: Physica C – volume: 86 start-page: 2278 year: 1998 publication-title: Proc. IEEE – volume: 9 year: 2021 publication-title: IEEE Access – ident: e_1_2_9_42_1 – ident: e_1_2_9_44_1 doi: 10.1103/PhysRevB.108.214428 – ident: e_1_2_9_34_1 doi: 10.1098/rspl.1895.0041 – ident: e_1_2_9_38_1 doi: 10.1088/2632-2153/acf6a9 – volume-title: Electron Energy Loss Spectroscopy in the Electron Microscope ident: e_1_2_9_6_1 – ident: e_1_2_9_8_1 doi: 10.1016/0304-3991(89)90304-5 – ident: e_1_2_9_10_1 doi: 10.1103/PhysRevLett.99.047203 – ident: e_1_2_9_9_1 doi: 10.1016/0921-4534(93)90501-G – ident: e_1_2_9_14_1 doi: 10.1016/j.apsadv.2023.100523 – year: 2022 ident: e_1_2_9_21_1 publication-title: arXiv – ident: e_1_2_9_1_1 doi: 10.1021/nl1022139 – ident: e_1_2_9_3_1 doi: 10.1038/416826a – year: 2018 ident: e_1_2_9_18_1 publication-title: arXiv – ident: e_1_2_9_41_1 doi: 10.1007/978-1-4419-7200-2 – ident: e_1_2_9_27_1 doi: 10.1177/14759217211073301 – ident: e_1_2_9_4_1 doi: 10.1023/A:1021428003587 – volume: 114 year: 2015 ident: e_1_2_9_11_1 publication-title: Phys. Rev. Lett. – ident: e_1_2_9_36_1 – ident: e_1_2_9_43_1 – ident: e_1_2_9_13_1 doi: 10.1021/acs.chemmater.3c02773 – ident: e_1_2_9_19_1 doi: 10.1186/s40679-018-0052-y – ident: e_1_2_9_24_1 doi: 10.1088/2632-2153/ad073b – ident: e_1_2_9_26_1 doi: 10.1038/s41598-021-97668-8 – ident: e_1_2_9_29_1 doi: 10.3389/fnins.2021.652987 – ident: e_1_2_9_39_1 doi: 10.1109/ACCESS.2018.2848210 – ident: e_1_2_9_20_1 doi: 10.1126/science.1127647 – ident: e_1_2_9_28_1 doi: 10.1016/j.cviu.2020.102920 – ident: e_1_2_9_16_1 doi: 10.1126/science.aaa8415 – ident: e_1_2_9_35_1 doi: 10.1109/TSMC.1979.4310076 – year: 2019 ident: e_1_2_9_40_1 publication-title: Pytorch: An imperative style, high‐performance deep learning library – ident: e_1_2_9_15_1 doi: 10.1038/nature14539 – ident: e_1_2_9_22_1 doi: 10.1103/RevModPhys.91.045002 – ident: e_1_2_9_31_1 doi: 10.1093/mam/ozae044.180 – ident: e_1_2_9_32_1 doi: 10.1109/5.726791 – year: 2022 ident: e_1_2_9_37_1 publication-title: arXiv – ident: e_1_2_9_23_1 doi: 10.1109/TGRS.2019.2908756 – ident: e_1_2_9_33_1 – ident: e_1_2_9_25_1 doi: 10.1038/s42256-022-00555-8 – ident: e_1_2_9_7_1 doi: 10.1103/PhysRevLett.92.095502 – ident: e_1_2_9_30_1 doi: 10.1002/smll.202205977 – ident: e_1_2_9_2_1 doi: 10.1038/nature03644 – ident: e_1_2_9_45_1 doi: 10.1109/ACCESS.2021.3065838 – ident: e_1_2_9_5_1 doi: 10.1038/336565a0 – volume: 11 year: 2021 ident: e_1_2_9_17_1 publication-title: Sci. Rep. – ident: e_1_2_9_12_1 doi: 10.1063/1.1330572 |
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| Snippet | A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging... A 3D Convolutional Variational Autoencoder (3D-CVAE) is introduced for automated anomaly detection in electron energy-loss spectroscopy spectrum imaging... |
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| SubjectTerms | Anomalies anomaly detection Automation convolutional variational autoencoders Defects Electron energy electron energy loss spectroscopy Image reconstruction Performance evaluation Principal components analysis Robustness Spectra spectral anomalies spectral imaging Spectrum analysis |
| Title | Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders |
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