{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:03:00Z","timestamp":1776182580111,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_4","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"35-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":268,"title":["Diffusion Models for\u00a0Medical Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Julia","family":"Wolleb","sequence":"first","affiliation":[]},{"given":"Florentin","family":"Bieder","sequence":"additional","affiliation":[]},{"given":"Robin","family":"Sandk\u00fchler","sequence":"additional","affiliation":[]},{"given":"Philippe C.","family":"Cattin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"4_CR1","unstructured":"Arun, N.T., et al.: Assessing the validity of saliency maps for abnormality localization in medical imaging. arXiv preprint arXiv:2006.00063 (2020)"},{"issue":"1","key":"4_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"4_CR3","unstructured":"Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"4_CR4","unstructured":"Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representations (2022)"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X., Konukoglu, E.: Visual feature attribution using Wasserstein GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8309\u20138319 (2018)","DOI":"10.1109\/CVPR.2018.00867"},{"key":"4_CR6","unstructured":"Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972 (2018)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, S., Jeong, Y., Gwon, Y., Yoon, S.: ILVR: conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938 (2021)","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"4_CR8","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"4_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"4_CR10","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, no. 6840\u20136851 (2020)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Kim, B., Han, I., Ye, J.C.: DiffuseMorph: unsupervised deformable image registration along continuous trajectory using diffusion models. arXiv preprint arXiv:2112.05149 (2021)","DOI":"10.1007\/978-3-031-19821-2_20"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., Welling, M.: An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691 (2019)","DOI":"10.1561\/9781680836233"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Marimont, S.N., Tarroni, G.: Anomaly detection through latent space restoration using vector quantized variational autoencoders. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1764\u20131767. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433778"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain MRI. arXiv preprint arXiv:2109.06023 (2021)","DOI":"10.1007\/978-3-031-08999-2_5"},{"issue":"10","key":"4_CR16","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR17","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: Proceedings of the 38th International Conference on Machine Learning, vol. 139, pp. 8162\u20138171. PMLR (2021)"},{"issue":"1","key":"4_CR18","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"110190","DOI":"10.1016\/j.chaos.2020.110190","volume":"140","author":"H Panwar","year":"2020","unstructured":"Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Bhardwaj, P., Singh, V.: A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-scan images. Chaos Solitons Fractals 140, 110190 (2020)","journal-title":"Chaos Solitons Fractals"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Unsupervised brain anomaly detection and segmentation with transformers. arXiv preprint arXiv:2102.11650 (2021)","DOI":"10.1016\/j.media.2022.102475"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Pirnay, J., Chai, K.: Inpainting transformer for anomaly detection. arXiv preprint arXiv:2104.13897 (2021)","DOI":"10.1007\/978-3-031-06430-2_33"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: image-to-image diffusion models. arXiv preprint arXiv:2111.05826 (2021)","DOI":"10.1145\/3528233.3530757"},{"key":"4_CR23","unstructured":"Sasaki, H., Willcocks, C.G., Breckon, T.P.: UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models. arXiv preprint arXiv:2104.05358 (2021)"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Siddiquee, M.M.R., et al.: Learning fixed points in generative adversarial networks: from image-to-image translation to disease detection and localization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 191\u2013200 (2019)","DOI":"10.1109\/ICCV.2019.00028"},{"key":"4_CR25","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"4_CR26","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)"},{"key":"4_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-030-59719-1_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Wolleb","year":"2020","unstructured":"Wolleb, J., Sandk\u00fchler, R., Cattin, P.C.: DeScarGAN: disease-specific anomaly detection with weak supervision. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 14\u201324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_2"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Yang, J., Xu, R., Qi, Z., Shi, Y.: Visual anomaly detection for images: a survey. arXiv preprint arXiv:2109.13157 (2021)","DOI":"10.1016\/j.procs.2022.01.057"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665\u2013674 (2017)","DOI":"10.1145\/3097983.3098052"},{"key":"4_CR30","unstructured":"Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:42:08Z","timestamp":1710243728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"3","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}