{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T06:26:43Z","timestamp":1791008803202,"version":"4.1.0"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012236","name":"Beijing Institute of Technology Research Fund Program for Young Scholars","doi-asserted-by":"publisher","award":["6120220236"],"award-info":[{"award-number":["6120220236"]}],"id":[{"id":"10.13039\/501100012236","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072007"],"award-info":[{"award-number":["82072007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["IS23112"],"award-info":[{"award-number":["IS23112"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.compmedimag.2024.102366","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T03:11:37Z","timestamp":1709694697000},"page":"102366","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"special_numbering":"C","title":["PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder"],"prefix":"10.1016","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3532-7498","authenticated-orcid":false,"given":"Shuai","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6633-1801","authenticated-orcid":false,"given":"Weihang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanruo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-3374","authenticated-orcid":false,"given":"Huiqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2024.102366_b1","series-title":"Computer Vision\u2013ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2\u20136, 2018, Revised Selected Papers, Part III 14","first-page":"622","article-title":"Ganomaly: Semi-supervised anomaly detection via adversarial training","author":"Akcay","year":"2019"},{"key":"10.1016\/j.compmedimag.2024.102366_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101952","article-title":"Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study","volume":"69","author":"Baur","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102366_b3","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4","first-page":"161","article-title":"Deep autoencoding models for unsupervised anomaly segmentation in brain MR images","author":"Baur","year":"2019"},{"key":"10.1016\/j.compmedimag.2024.102366_b4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C., 2020. Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"10.1016\/j.compmedimag.2024.102366_b5","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102366_b6","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume":"vol. 119","author":"Chen","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102366_b7","series-title":"2018 Wireless Telecommunications Symposium","first-page":"1","article-title":"Autoencoder-based network anomaly detection","author":"Chen","year":"2018"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102366_b8","article-title":"Brain tumor dataset","volume":"1512427","author":"Cheng","year":"2017","journal-title":"Figshare Dataset"},{"key":"10.1016\/j.compmedimag.2024.102366_b9","doi-asserted-by":"crossref","unstructured":"Deng, H., Li, X., 2022. Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9737\u20139746.","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"10.1016\/j.compmedimag.2024.102366_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2019.101660","article-title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","volume":"79","author":"Dolz","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102366_b11","article-title":"Encoder-decoder contrast for unsupervised anomaly detection in medical images","author":"Guo","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102366_b12","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020a. Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.compmedimag.2024.102366_b13","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020b. Momentum Contrast for Unsupervised Visual Representation Learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.compmedimag.2024.102366_b14","series-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102366_b15","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"10.1016\/j.compmedimag.2024.102366_b16","series-title":"Anomaly detection for skin disease images using variational autoencoder","author":"Lu","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102366_b17","series-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2019"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102366_b18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10.1016\/j.compmedimag.2024.102366_b19","series-title":"International Conference on Machine Learning","first-page":"4393","article-title":"Deep one-class classification","author":"Ruff","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102366_b20","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R., 2021. Multiresolution Knowledge Distillation for Anomaly Detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. CVPR, pp. 14902\u201314912.","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"10.1016\/j.compmedimag.2024.102366_b21","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","article-title":"f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks","volume":"54","author":"Schlegl","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102366_b22","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"Schlegl","year":"2017"},{"issue":"7","key":"10.1016\/j.compmedimag.2024.102366_b23","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Sch\u00f6lkopf","year":"2001","journal-title":"Neural Comput."},{"key":"10.1016\/j.compmedimag.2024.102366_b24","series-title":"Proceedings of the 32nd International Conference on Machine Learning","first-page":"843","article-title":"Unsupervised learning of video representations using LSTMs","volume":"vol. 37","author":"Srivastava","year":"2015"},{"key":"10.1016\/j.compmedimag.2024.102366_b25","series-title":"Student-teacher feature pyramid matching for anomaly detection","author":"Wang","year":"2021"},{"key":"10.1016\/j.compmedimag.2024.102366_b26","doi-asserted-by":"crossref","unstructured":"Yi, J., Yoon, S., 2020. Patch svdd: Patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision.","DOI":"10.1007\/978-3-030-69544-6_23"},{"key":"10.1016\/j.compmedimag.2024.102366_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102257","article-title":"MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification","author":"Zhang","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"issue":"12","key":"10.1016\/j.compmedimag.2024.102366_b28","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/TMI.2021.3093883","article-title":"Anomaly detection for medical images using self-supervised and translation-consistent features","volume":"40","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102366_b29","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TMI.2021.3118223","article-title":"Proxy-bridged image reconstruction network for anomaly detection in medical images","volume":"41","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124000430?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124000430?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T05:33:50Z","timestamp":1760247230000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611124000430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":29,"alternative-id":["S0895611124000430"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102366","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102366","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102366"}}