{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:02Z","timestamp":1772908622165,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s00521-022-08029-z","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T18:02:33Z","timestamp":1670608953000},"page":"23025-23040","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Threat Object-based anomaly detection in X-ray images using GAN-based ensembles"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8613-0361","authenticated-orcid":false,"given":"Shreyas","family":"Kolte","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neelanjan","family":"Bhowmik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Dhiraj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"8029_CR1","unstructured":"Goodfellow I et al (2014) Generative adversarial nets. In: NIPS\u201914: Proceedings of the 27th international conference on neural information processing systems, vol 2, pp 2672\u20132680. http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf"},{"key":"8029_CR2","doi-asserted-by":"crossref","unstructured":"Schlegl T et al (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Proceedings of Information Processing in Medical Imaging (IPMI) (2017), lecture notes in computer science, vol 10265, pp 146\u2013157","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"8029_CR3","unstructured":"Zenati H et al (2018) Efficient GAN-based anomaly detection. In: ICDM. arXiv:1812.02288"},{"key":"8029_CR4","unstructured":"Donahue J et al (2017) Adversarial feature learning. In: ICLR. arXiv:1605.09782"},{"key":"8029_CR5","doi-asserted-by":"crossref","unstructured":"Ackay S et al (2018) Semi-supervised anomaly detection via adversarial training. In: Asian Conference on Computer Vision (ACCV). Lecture notes in computer science, vol 11363, pp 622\u2013637. arxiv:1805.06725v3","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"8029_CR6","unstructured":"Radford A et al (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR. arXiv:1511.06434"},{"key":"8029_CR7","doi-asserted-by":"crossref","unstructured":"Ackay S et al (2019) Skip-GANomaly: skip conected and adversarially trained encoder-decoder anomaly detection. In: International Joint Conference on Neural Networks (IJCNN). IEEE","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"8029_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger O et al (2015) Convolutional neural networks for biomedical image segmentation. In: International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8029_CR9","unstructured":"Davletshina D et al (2020) Unsupervised anomaly detection for X-ray images. arXiv:2001.10883"},{"key":"8029_CR10","doi-asserted-by":"crossref","unstructured":"Han X et al (2021) GAN ensemble for anomaly detection. In: AAAI. arXiv:2012.07988v1 [cs.LG]. 14 Dec 2020","DOI":"10.1609\/aaai.v35i5.16530"},{"key":"8029_CR11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12859-020-03936-1","volume":"22","author":"C Han","year":"2021","unstructured":"Han C, Rundo L, Murao K et al (2021) MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform 22:31","journal-title":"BMC Bioinform"},{"key":"8029_CR12","doi-asserted-by":"crossref","unstructured":"Bengs M, Behrendt F, Laves M-H, Kr\u00fcger J, Opfer R, Schlaefer A (2022) Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction. In: Proceedings of SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203314","DOI":"10.1117\/12.2608120"},{"key":"8029_CR13","doi-asserted-by":"crossref","unstructured":"Jensen S et al (2022) Deep learning-based anomaly detection on X-ray images of fuel cell electrodes. In: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2022)","DOI":"10.5220\/0010785400003124"},{"key":"8029_CR14","doi-asserted-by":"publisher","first-page":"8602","DOI":"10.1038\/s41598-021-87994-2","volume":"11","author":"S Motamed","year":"2021","unstructured":"Motamed S, Rogalla P, Randgan KF (2021) Randomized generative adversarial network for detection of COVID-19 in chest X-ray. Sci Rep 11:8602. https:\/\/doi.org\/10.1038\/s41598-021-87994-2","journal-title":"Sci Rep"},{"key":"8029_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3082158","author":"T Jiang","year":"2021","unstructured":"Jiang T, Xie W, Li Y, Lei J, Du Q (2021) Weakly supervised discriminative learning with spectral constrained generative adversarial network for hyperspectral anomaly detection. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3082158","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8029_CR16","unstructured":"Song J, Kong K, Park Y-I, Kim, S-G, Kang S-J (2021) AnoSeg: anomaly segmentation network using self-supervised learning"},{"key":"8029_CR17","doi-asserted-by":"crossref","unstructured":"Matthew C, Griffin LD (2019) Limits on transfer learning from photographic image data to X-ray threat detection. J X-ray Sci Technol 27(6):1007\u20131020","DOI":"10.3233\/XST-190545"},{"key":"8029_CR18","doi-asserted-by":"crossref","unstructured":"Miao C et al (2019) SIXray: a large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00222"},{"key":"8029_CR19","doi-asserted-by":"publisher","unstructured":"Zhou Z et al (2019) UNet++: a nested U-Net architecture for medical image segmentation. In: D. Stoyanov et al. (eds) DLMIA 2018\/ML-CDS 2018, LNCS 11045, pp 3\u201311. https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"8029_CR20","unstructured":"Leland M et al (2018) UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08029-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08029-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08029-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T14:24:42Z","timestamp":1744208682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08029-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":20,"journal-issue":{"issue":"31","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["8029"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08029-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]},"assertion":[{"value":"10 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We, the authors, certify that we have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}