{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:22:05Z","timestamp":1792783325897,"version":"4.5.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"JSPS KAKENHI","award":["24K15776"],"award-info":[{"award-number":["24K15776"]}]},{"DOI":"10.13039\/501100005405","name":"Ritsumeikan University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005405","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol Phys Technol"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study aims to develop an anomaly-detection scheme for lesions in CT images. Our database consists of lung CT images obtained from 1500 examinees. It includes 1200 normal and 300 abnormal cases. In this study, SVDD (Support Vector Data Description) mapping the normal latent variables into a hypersphere as small as possible on the latent space is introduced to VQ-VAE (Vector Quantized-Variational Auto-Encoder). VQ-VAE with SVDD is constructed from two encoders, two decoders, and an embedding space. The first encoder compresses the input image into the latent-variable map, whereas the second encoder maps the normal latent variables into a hypersphere as small as possible. The first decoder then up-samples the mapped latent variables into a latent-variable map with the original size. The second decoder finally reconstructs the input image from the latent-variable map replaced by the embedding representations. The data of each examinee is classified as abnormal or normal based on the anomaly score defined as the combination of the difference between the input image and the reconstructed image and the distance between the latent variables and the center of the hypersphere. The area under the ROC curve for VQ-VAE with SVDD was 0.76, showing an improvement when compared with the conventional VAE (0.63,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.001). VQ-VAE with SVDD developed in this study can yield higher anomaly-detection accuracy than the conventional VAE. The proposed method is expected to be useful for identifying examinees with lesions and reducing interpretation time in CT screening.\n                  <\/jats:p>","DOI":"10.1007\/s12194-024-00851-5","type":"journal-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T04:05:41Z","timestamp":1729915541000},"page":"17-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Anomaly detection scheme for lung CT images using vector quantized variational auto-encoder with support vector data description"],"prefix":"10.1007","volume":"18","author":[{"given":"Zhihui","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0181-7496","authenticated-orcid":false,"given":"Ryohei","family":"Nakayama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akiyoshi","family":"Hizukuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoji","family":"Kido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"851_CR1","doi-asserted-by":"publisher","first-page":"100179","DOI":"10.1016\/j.lanepe.2021.100179","volume":"10","author":"JK Field","year":"2021","unstructured":"Field JK, Vulkan D, Davies MPA, et al. Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis. Lancet Reg Health Eur. 2021;10:100179. https:\/\/doi.org\/10.1016\/j.lanepe.2021.100179.","journal-title":"Lancet Reg Health Eur"},{"issue":"6","key":"851_CR2","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1056\/nejmoa1911793","volume":"382","author":"HJ de Koning","year":"2020","unstructured":"de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503\u201313. https:\/\/doi.org\/10.1056\/nejmoa1911793.","journal-title":"N Engl J Med"},{"issue":"11","key":"851_CR3","doi-asserted-by":"publisher","first-page":"7976","DOI":"10.1007\/s00330-022-08741-3","volume":"32","author":"S Noguchi","year":"2022","unstructured":"Noguchi S, Nishio M, Sakamoto R, et al. Deep learning-based algorithm improved radiologists\u2019 performance in bone metastases detection on CT. Eur Radiol. 2022;32(11):7976\u201387. https:\/\/doi.org\/10.1007\/s00330-022-08741-3.","journal-title":"Eur Radiol"},{"issue":"1","key":"851_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.radi.2022.10.011","volume":"29","author":"JWL Ooi","year":"2023","unstructured":"Ooi JWL, Ng SY, Khor CC, et al. Service evaluation of radiographer-led vetting and protocoling of computed tomography (CT) scan requests in a Singapore public healthcare institution. Radiography (Lond). 2023;29(1):139\u201344. https:\/\/doi.org\/10.1016\/j.radi.2022.10.011.","journal-title":"Radiography (Lond)"},{"issue":"10","key":"851_CR5","doi-asserted-by":"publisher","first-page":"936","DOI":"10.3348\/kjr.2023.0742","volume":"24","author":"NM Ramli","year":"2023","unstructured":"Ramli NM, Mohd Zain NR. The growing problem of radiologist shortage: malaysia\u2019s perspective. Korean J Radiol. 2023;24(10):936\u20137.","journal-title":"Korean J Radiol"},{"key":"851_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2023.08.046","author":"H Jk","year":"2023","unstructured":"Jk H, Kyung HD. The staffing crisis and burnout in academic radiology: insights from a survey study in Korea. J Am Coll Radiol. 2023. https:\/\/doi.org\/10.1016\/j.jacr.2023.08.046.","journal-title":"J Am Coll Radiol"},{"key":"851_CR7","doi-asserted-by":"publisher","unstructured":"Tafti AP, Bashiri FS, LaRose E, Peissig P. 2018 Diagnostic classification of lung CT images using deep 3D multi-scale Convolutional neural network. 2018 IEEE international Conference on healthcare informatics (ICHI). https:\/\/doi.org\/10.1109\/ICHI.2018.00078","DOI":"10.1109\/ICHI.2018.00078"},{"issue":"2","key":"851_CR8","doi-asserted-by":"publisher","first-page":"151","DOI":"10.26555\/ijain.v7i2.317","volume":"7","author":"A Saleh","year":"2021","unstructured":"Saleh A, Chin C, Penshie V, Al-Absi H. Lung cancer medical images classification using hybrid CNN-SVM. Int J Adv Intell Informat. 2021;7(2):151\u201362. https:\/\/doi.org\/10.26555\/ijain.v7i2.317.","journal-title":"Int J Adv Intell Informat"},{"key":"851_CR9","doi-asserted-by":"publisher","first-page":"102527","DOI":"10.1016\/j.bspc.2021.102527","volume":"67","author":"P Dutande","year":"2021","unstructured":"Dutande P, Baid U, Talbar S. LNCDS: A 2D\u20133D cascaded CNN approach for lung nodule classification, detection and segmentation. Biomed Signal Process Control. 2021;67:102527. https:\/\/doi.org\/10.1016\/j.bspc.2021.102527.","journal-title":"Biomed Signal Process Control"},{"key":"851_CR10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1701.00160","author":"IJ Goodfellow","year":"2016","unstructured":"Goodfellow IJ, NIPS. Tutoria: generative adversarial network. arXiv. 2016. https:\/\/doi.org\/10.48550\/arXiv.1701.00160.","journal-title":"arXiv"},{"key":"851_CR11","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114","author":"DP Kingma","year":"2022","unstructured":"Kingma DP, Welling M. Auto-encodering variational bayes. arXiv. 2022. https:\/\/doi.org\/10.48550\/arXiv.1312.6114.","journal-title":"arXiv"},{"issue":"2","key":"851_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, H AVD. Deep learning for anomaly detection: a review. ACM Comput Surv. 2021;54(2):1\u201338. https:\/\/doi.org\/10.1145\/3439950.","journal-title":"ACM Comput Surv"},{"key":"851_CR13","doi-asserted-by":"publisher","first-page":"104436","DOI":"10.1016\/j.jbi.2023.104436","volume":"144","author":"NI Kuo","year":"2023","unstructured":"Kuo NI, Garcia F, S\u00f6nnerborg A, B\u00f6hm M, et al. Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV. J Biomed Inform. 2023;144:104436. https:\/\/doi.org\/10.1016\/j.jbi.2023.104436.","journal-title":"J Biomed Inform"},{"issue":"1","key":"851_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00354-022-00194-y","volume":"41","author":"S Chatterjee","year":"2023","unstructured":"Chatterjee S, Maity S, Bhattacharjee M, Banerjee S, Das AK, Ding W. Variational autoencoder based imbalanced COVID-19 detection using chest X-Ray images. New Gener Comput. 2023;41(1):25\u201360. https:\/\/doi.org\/10.1007\/s00354-022-00194-y.","journal-title":"New Gener Comput"},{"issue":"3","key":"851_CR15","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/1206212X.2022.2143026","volume":"45","author":"I Touseef","year":"2023","unstructured":"Touseef I, Shaima Q. Reconstruction probability-based anomaly detection using variational auto-encoders. Int J Comput Appl. 2023;45(3):231\u20137. https:\/\/doi.org\/10.1080\/1206212X.2022.2143026.","journal-title":"Int J Comput Appl"},{"key":"851_CR16","first-page":"5443","volume":"34","author":"Y Wang","year":"2021","unstructured":"Wang Y, Blei D, Cunningham JP. Posterior collapse and latent variable non-identifiability. Adv Neural Inf Process Syst. 2021;34:5443\u201355.","journal-title":"Adv Neural Inf Process Syst"},{"key":"851_CR17","unstructured":"Oord AVD, Vinyals O, Kavukcuoglu K. 2017. Neural discrete representation learning. Proceeding of the 31st International Conference on Neural Information Processing Systems. pp.6309\u20136318."},{"issue":"1","key":"851_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"D Tax","year":"2004","unstructured":"Tax D, Duin R. Support vector data description. Machine-mediated Learning. 2004;54(1):45\u201366. https:\/\/doi.org\/10.1023\/B:MACH.0000008084.60811.49.","journal-title":"Machine-mediated Learning"},{"issue":"3","key":"851_CR19","doi-asserted-by":"publisher","first-page":"337","DOI":"10.2307\/1990404","volume":"68","author":"N Aronszajn","year":"1950","unstructured":"Aronszajn N. Theory of reproducing kernels. Trans Am Math Soc. 1950;68(3):337\u2013404. https:\/\/doi.org\/10.2307\/1990404.","journal-title":"Trans Am Math Soc"},{"issue":"18","key":"851_CR20","doi-asserted-by":"publisher","first-page":"3292","DOI":"10.3390\/math10183292","volume":"10","author":"Q Huang","year":"2022","unstructured":"Huang Q, Zheng Z, Zhu W, et al. Anomaly detection algorithm based on broad learning system and support vector domain description. Mathematics. 2022;10(18):3292. https:\/\/doi.org\/10.3390\/math10183292.","journal-title":"Mathematics"},{"key":"851_CR21","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2021.04.089","volume":"453","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Liang X, Zhang W, Zhang L, Song X. VAE-based deep SVDD for anomaly detection. Neurocomputing. 2021;453:131\u201340. https:\/\/doi.org\/10.1016\/j.neucom.2021.04.089.","journal-title":"Neurocomputing"},{"key":"851_CR22","doi-asserted-by":"publisher","first-page":"1398","DOI":"10.1109\/ACSSC.2003.1292216","volume":"2","author":"Z Wang","year":"2003","unstructured":"Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment. Thrity-Seventh Asilomar Conf Signal, Syst Comput. 2003;2:1398\u2013402. https:\/\/doi.org\/10.1109\/ACSSC.2003.1292216.","journal-title":"Thrity-Seventh Asilomar Conf Signal, Syst Comput"},{"issue":"1","key":"851_CR23","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao H, Gallo O, Frosio I, Kautz J. Loss functions for image restoration with neural networks. IEEE Transact Computat Imag. 2017;3(1):47\u201357. https:\/\/doi.org\/10.1109\/TCI.2016.2644865.","journal-title":"IEEE Transact Computat Imag"},{"issue":"2","key":"851_CR24","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/s10278-006-0264-x","volume":"19","author":"VA Magnotta","year":"2006","unstructured":"Magnotta VA, Friedman L, BIRN F. Measurement of signal-tonoise and contrast-to-noise in the fBIRNmulticenter imaging study. J Digit Imag. 2006;19(2):140\u20137.","journal-title":"J Digit Imag"},{"key":"851_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2014.6830420","author":"HA Dincer","year":"2014","unstructured":"Dincer HA, Gokcay D. Evaluation of MRIS of the brain with respect to SNR, CNR and GWR in young versus old subjects. Signal Proces Communicat Appl Conf. 2014. https:\/\/doi.org\/10.1109\/SIU.2014.6830420.","journal-title":"Signal Proces Communicat Appl Conf"}],"container-title":["Radiological Physics and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12194-024-00851-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12194-024-00851-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12194-024-00851-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:50:55Z","timestamp":1741002655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12194-024-00851-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["851"],"URL":"https:\/\/doi.org\/10.1007\/s12194-024-00851-5","relation":{},"ISSN":["1865-0333","1865-0341"],"issn-type":[{"value":"1865-0333","type":"print"},{"value":"1865-0341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"1 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical approval for case use was obtained from the institutional review board (IRB) of both the authors\u2019 institutions.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}