{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T02:37:52Z","timestamp":1773023872305,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031456725","type":"print"},{"value":"9783031456732","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-45673-2_37","type":"book-chapter","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T08:02:16Z","timestamp":1697270536000},"page":"372-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Unsupervised Anomaly Detection in\u00a0Medical Images Using Masked Diffusion Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2162-3367","authenticated-orcid":false,"given":"Hasan","family":"Iqbal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3357-9720","authenticated-orcid":false,"given":"Umar","family":"Khalid","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3957-7061","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3981-2933","authenticated-orcid":false,"given":"Jing","family":"Hua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"37_CR1","unstructured":"https:\/\/brain-development.org\/ixi-dataset\/"},{"key":"37_CR2","unstructured":"Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021)","DOI":"10.1016\/j.media.2020.101952"},{"key":"37_CR4","unstructured":"Behrendt, F., Bengs, M., Bhattacharya, D., Kr\u00fcger, J., Opfer, R., Schlaefer, A.: Capturing inter-slice dependencies of 3D brain MRI-scans for unsupervised anomaly detection. In: Medical Imaging with Deep Learning (2022)"},{"key":"37_CR5","unstructured":"Behrendt, F., Bhattacharya, D., Kr\u00fcger, J., Opfer, R., Schlaefer, A.: Patched diffusion models for unsupervised anomaly detection in brain MRI. arXiv preprint arXiv:2303.03758 (2023)"},{"issue":"9","key":"37_CR6","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11548-021-02451-9","volume":"16","author":"M Bengs","year":"2021","unstructured":"Bengs, M., Behrendt, F., Kr\u00fcger, J., Opfer, R., Schlaefer, A.: Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI. Int. J. Comput. Assist. Radiol. Surg. 16(9), 1413\u20131423 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02451-9","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"37_CR7","unstructured":"Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. In: International Conference on Medical Imaging with Deep Learning (MIDL). Proceedings of Machine Learning Research, PMLR (2018)"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"issue":"3","key":"37_CR10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"37_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2022.100068","volume":"6","author":"RJ Ellis","year":"2022","unstructured":"Ellis, R.J., Sander, R.M., Limon, A.: Twelve key challenges in medical machine learning and solutions. Intell.-Based Med. 6, 100068 (2022)","journal-title":"Intell.-Based Med."},{"issue":"7","key":"37_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3464423","volume":"54","author":"T Fernando","year":"2021","unstructured":"Fernando, T., Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Deep learning for medical anomaly detection - a survey. ACM Comput. Surv. 54(7), 1\u201337 (2021). https:\/\/doi.org\/10.1145\/3464423","journal-title":"ACM Comput. Surv."},{"key":"37_CR13","unstructured":"Gao, P., Ma, T., Li, H., Lin, Z., Dai, J., Qiao, Y.: ConvMAE: masked convolution meets masked autoencoders (2022)"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners (2021)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"37_CR15","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"issue":"1","key":"37_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0192-5","journal-title":"J. Big Data"},{"key":"37_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759","volume":"65","author":"D Karimi","year":"2020","unstructured":"Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)","journal-title":"Med. Image Anal."},{"key":"37_CR18","unstructured":"Kascenas, A., Pugeault, N., O\u2019Neil, A.Q.: Denoising autoencoders for unsupervised anomaly detection in brain MRI. In: International Conference on Medical Imaging with Deep Learning (MIDL). Proceedings of Machine Learning Research, PMLR (2022)"},{"key":"37_CR19","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fnint.2014.00037","volume":"8","author":"L Kauffmann","year":"2014","unstructured":"Kauffmann, L., Ramano\u00ebl, S., Peyrin, C.: The neural bases of spatial frequency processing during scene perception. Front. Integr. Neurosci. 8, 37 (2014)","journal-title":"Front. Integr. Neurosci."},{"issue":"1","key":"37_CR20","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s12021-017-9348-7","volume":"16","author":"\u017d Lesjak","year":"2017","unstructured":"Lesjak, \u017d, et al.: A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus. Neuroinformatics 16(1), 51\u201363 (2017). https:\/\/doi.org\/10.1007\/s12021-017-9348-7","journal-title":"Neuroinformatics"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: RePaint: inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11461\u201311471 (2022)","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen, B., Feldman, A., Bethapudi, S., Jennings, A., Willcocks, C.G.: Unsupervised region-based anomaly detection in brain MRI with adversarial image inpainting. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1127\u20131131. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434115"},{"key":"37_CR23","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-662-00551-4_4","volume-title":"Fast Fourier Transform and Convolution Algorithms","author":"HJ Nussbaumer","year":"1981","unstructured":"Nussbaumer, H.J.: The fast Fourier transform. In: Fast Fourier Transform and Convolution Algorithms, pp. 80\u2013111. Springer, Berlin, Heidelberg (1981). https:\/\/doi.org\/10.1007\/978-3-662-00551-4_4"},{"issue":"8","key":"37_CR24","doi-asserted-by":"publisher","first-page":"10346","DOI":"10.1109\/TPAMI.2023.3238179","volume":"45","author":"O \u00d6zdenizci","year":"2023","unstructured":"\u00d6zdenizci, O., Legenstein, R.: Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 10346\u201310357 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H., et al.: Fast unsupervised brain anomaly detection and segmentation with diffusion models. arXiv preprint arXiv:2206.03461 (2022)","DOI":"10.1016\/j.media.2022.102475"},{"key":"37_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102475","volume":"79","author":"WH Pinaya","year":"2022","unstructured":"Pinaya, W.H., et al.: Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. Med. Image Anal. 79, 102475 (2022)","journal-title":"Med. Image Anal."},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"37_CR28","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-031-18576-2_4","volume-title":"Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings","author":"P Sanchez","year":"2022","unstructured":"Sanchez, P., Kascenas, A., Liu, X., O\u2019Neil, A.Q., Tsaftaris, S.A.: What is healthy? generative counterfactual diffusion for lesion localization. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, pp. 34\u201344. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18576-2_4"},{"key":"37_CR29","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","journal-title":"Med. Image Anal."},{"key":"37_CR30","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"37_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102526","volume":"80","author":"J Silva-Rodr\u00edguez","year":"2022","unstructured":"Silva-Rodr\u00edguez, J., Naranjo, V., Dolz, J.: Constrained unsupervised anomaly segmentation. Med. Image Anal. 80, 102526 (2022)","journal-title":"Med. Image Anal."},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: FreMAE: Fourier transform meets masked autoencoders for medical image segmentation (2023)","DOI":"10.1109\/WACV57701.2024.00768"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Wolleb, J., Bieder, F., Sandk\u00fchler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. arXiv preprint arXiv:2203.04306 (2022)","DOI":"10.1007\/978-3-031-16452-1_4"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Wyatt, J., Leach, A., Schmon, S.M., Willcocks, C.G.: AnoDDPM: anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 650\u2013656 (2022)","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"37_CR35","unstructured":"Zimmerer, D., Kohl, S., Petersen, J., Isensee, F., Maier-Hein, K.: Context-encoding variational autoencoder for unsupervised anomaly detection. In: International Conference on Medical Imaging with Deep Learning-Extended Abstract Track (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45673-2_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T21:03:19Z","timestamp":1730322199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45673-2_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"ISBN":["9783031456725","9783031456732"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45673-2_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,15]]},"assertion":[{"value":"15 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2023?pli=1","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"139","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"93","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}