{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:18:11Z","timestamp":1743106691075,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031443350"},{"type":"electronic","value":"9783031443367"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44336-7_18","type":"book-chapter","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T14:01:39Z","timestamp":1696600899000},"page":"177-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Confidence-Aware and\u00a0Self-supervised Image Anomaly Localisation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8636-7986","authenticated-orcid":false,"given":"Johanna","family":"P. M\u00fcller","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6252-7658","authenticated-orcid":false,"given":"Matthew","family":"Baugh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9769-068X","authenticated-orcid":false,"given":"Jeremy","family":"Tan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1061-8990","authenticated-orcid":false,"given":"Mischa","family":"Dombrowski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-5023","authenticated-orcid":false,"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"18_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"issue":"2","key":"18_CR2","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato, S.G., III., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"18_CR3","unstructured":"Baugh, M.: PIE-torch. www.github.com\/matt-baugh\/pytorch-poisson-image-editing"},{"key":"18_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-32245-8_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"CF Baumgartner","year":"2019","unstructured":"Baumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 119\u2013127. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_14"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1905\u20131909. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098686"},{"key":"18_CR6","unstructured":"Cao, T., Huang, C.W., Hui, D.Y.T., Cohen, J.P.: A benchmark of medical out of distribution detection. arXiv preprint arXiv:2007.04250 (2020)"},{"key":"18_CR7","unstructured":"Chen, X., Pawlowski, N., Rajchl, M., Glocker, B., Konukoglu, E.: Deep generative models in the real-world: an open challenge from medical imaging. arXiv preprint arXiv:1806.05452 (2018)"},{"key":"18_CR8","unstructured":"Fang, Z., Li, Y., Lu, J., Dong, J., Han, B., Liu, F.: Is out-of-distribution detection learnable? arXiv preprint arXiv:2210.14707 (2022)"},{"issue":"3","key":"18_CR9","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.3.031411","volume":"6","author":"S Guan","year":"2019","unstructured":"Guan, S., Loew, M.: Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J. Med. Imaging 6(3), 031411 (2019)","journal-title":"J. Med. Imaging"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Guo, X., Gichoya, J.W., Purkayastha, S., Banerjee, I.: CVAD: a generic medical anomaly detector based on cascade VAE. arXiv preprint arXiv:2110.15811 (2021)","DOI":"10.1007\/978-3-031-16760-7_18"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Han, C., et al.: Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection. In: 2019 International Conference on 3D Vision (3DV), pp. 729\u2013737. IEEE (2019)","DOI":"10.1109\/3DV.2019.00085"},{"key":"18_CR12","unstructured":"Henaff, O.: Data-efficient image recognition with contrastive predictive coding. In: International Conference on Machine Learning, pp. 4182\u20134192. PMLR (2020)"},{"key":"18_CR13","unstructured":"Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. arXiv preprint arXiv:1906.12340 (2019)"},{"key":"18_CR14","unstructured":"Johnson, A., et al.: MIMIC-CXR-JPG-chest radiographs with structured labels (2019)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Li, C.L., Sohn, K., Yoon, J., Pfister, T.: CutPaste: self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664\u20139674 (2021)","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"18_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-59861-7_10","volume-title":"Machine Learning in Medical Imaging","author":"X Li","year":"2020","unstructured":"Li, X., Lu, Y., Desrosiers, C., Liu, X.: Out-of-distribution detection for skin lesion images with deep isolation forest. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 91\u2013100. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59861-7_10"},{"key":"18_CR17","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Mohseni, S., Pitale, M., Yadawa, J., Wang, Z.: Self-supervised learning for generalizable out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5216\u20135223 (2020)","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Nakao, T., Hanaoka, S., Nomura, Y., Hayashi, N., Abe, O.: Anomaly detection in chest 18F-FDG PET\/CT by Bayesian deep learning. Japan. J. Radiol., 1\u201310 (2022)","DOI":"10.1007\/s11604-022-01249-2"},{"key":"18_CR20","unstructured":"Pawlowski, N., et al.: Unsupervised lesion detection in brain CT using Bayesian convolutional autoencoders (2018)"},{"key":"18_CR21","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":"18_CR22","unstructured":"Schl\u00fcter, H.M., Tan, J., Hou, B., Kainz, B.: Self-supervised out-of-distribution detection and localization with natural synthetic anomalies (NSA). arXiv preprint arXiv:2109.15222 (2021)"},{"issue":"1","key":"18_CR23","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TMI.2019.2919951","volume":"39","author":"P Seeb\u00f6ck","year":"2019","unstructured":"Seeb\u00f6ck, P., et al.: Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal oct. IEEE Trans. Med. Imaging 39(1), 87\u201398 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"18_CR24","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71\u201374 (2000)","journal-title":"Am. J. Roentgenol."},{"key":"18_CR25","unstructured":"Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B.: Detecting outliers with foreign patch interpolation. arXiv preprint arXiv:2011.04197 (2020)"},{"key":"18_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-030-87240-3_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Tan","year":"2021","unstructured":"Tan, J., Hou, B., Day, T., Simpson, J., Rueckert, D., Kainz, B.: Detecting outliers with poisson image interpolation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 581\u2013591. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_56"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Tschuchnig, M.E., Gadermayr, M.: Anomaly detection in medical imaging-a mini review. Data Sci.-Anal. Appl., 33\u201338 (2022)","DOI":"10.1007\/978-3-658-36295-9_5"},{"key":"18_CR28","unstructured":"Venkatakrishnan, A.R., Kim, S.T., Eisawy, R., Pfister, F., Navab, N.: Self-supervised out-of-distribution detection in brain CT scans. arXiv preprint arXiv:2011.05428 (2020)"},{"key":"18_CR29","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"},{"issue":"3","key":"18_CR30","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)","journal-title":"J. Med. Imaging"},{"key":"18_CR31","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)"},{"issue":"12","key":"18_CR32","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1109\/TMI.2021.3093883","volume":"40","author":"H Zhao","year":"2021","unstructured":"Zhao, H., et al.: Anomaly detection for medical images using self-supervised and translation-consistent features. IEEE Trans. Med. Imaging 40(12), 3641\u20133651 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR33","unstructured":"Zhou, L., Deng, W., Wu, X.: Unsupervised anomaly localization using VAE and beta-VAE. arXiv preprint arXiv:2005.10686 (2020)"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44336-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T12:05:30Z","timestamp":1703851530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44336-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031443350","9783031443367"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44336-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancover, 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":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","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":"21","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":"66% - 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":"3","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)"}}]}}