{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:20:22Z","timestamp":1757312422420,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031451690"},{"type":"electronic","value":"9783031451706"}],"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\/978-3-031-45170-6_37","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:03:02Z","timestamp":1699966982000},"page":"359-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-supervised Diffusion Model for\u00a0Anomaly Segmentation in Medical Imaging"],"prefix":"10.1007","author":[{"given":"Komal","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6958-1253","authenticated-orcid":false,"given":"Snehashis","family":"Chakraborty","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-9311","authenticated-orcid":false,"given":"Sudipta","family":"Roy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.imu.2018.02.006","volume":"13","author":"S Roy","year":"2018","unstructured":"Roy, S., Bhattacharyya, D., Bandyopadhyay, S.K., Kim, T.H.: Heterogeneity of human brain tumor with lesion identification, localization, and analysis from MRI. Inform. Med. Unlocked 13, 139\u2013150 (2018)","journal-title":"Inform. Med. Unlocked"},{"key":"37_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-27272-2_14","volume-title":"Image Analysis and Recognition","author":"S Roy","year":"2019","unstructured":"Roy, S., Shoghi, K.I.: Computer-aided tumor segmentation from T2-weighted MR images of patient-derived tumor xenografts. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11663, pp. 159\u2013171. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-27272-2_14"},{"key":"37_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1007\/978-3-031-20713-6_34","volume-title":"Advances in Visual Computing","author":"A Kabiraj","year":"2022","unstructured":"Kabiraj, A., Meena, T., Reddy, P.B., Roy, S.: Detection and classification of lung disease using deep learning architecture from x-ray images. In: Bebis, G., et al. (eds.) ISVC 2022. LNCS, vol. 13598, pp. 444\u2013455. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20713-6_34"},{"key":"37_CR4","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-981-19-6525-8_17","volume-title":"Soft Computing for Problem Solving","author":"K Kumar","year":"2023","unstructured":"Kumar, K., Kumar, H., Wadhwa, P.: Encoder-decoder (LSTM-LSTM) network-based prediction model for trend forecasting in currency market. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds.) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol. 547, pp. 211\u2013223. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-19-6525-8_17"},{"issue":"4","key":"37_CR5","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1561\/2200000056","volume":"12","author":"DP Kingma","year":"2019","unstructured":"Kingma, D.P., Welling, M.: An introduction to variational autoencoders. Found. Trends Mach. Learn. 12(4), 307 (2019)","journal-title":"Found. Trends Mach. Learn."},{"key":"37_CR6","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":"37_CR7","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"37_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1007\/978-3-031-06430-2_33","volume-title":"Image Analysis and Processing - ICIAP 2022","author":"J Pirnay","year":"2022","unstructured":"Pirnay, J., Chai, K.: Inpainting transformer for anomaly detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) ICIAP 2022. LNCS, vol. 13232, pp. 394\u2013406. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-06430-2_33"},{"key":"37_CR9","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":"37_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-031-08999-2_5","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes 2021","author":"F Meissen","year":"2021","unstructured":"Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain MRI. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes 2021. Lecture Notes in Computer Science, vol. 12962, pp. 63\u201374. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_5"},{"key":"37_CR11","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"37_CR12","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"37_CR13","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780\u20138794 (2021)"},{"key":"37_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-031-16452-1_4","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2022","author":"J Wolleb","year":"2022","unstructured":"Wolleb, J., Bieder, F., Sandk\u00fchler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. Lecture Notes in Computer Science, vol. 13438, pp. 35\u201345. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_4"},{"key":"37_CR15","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_CR16","doi-asserted-by":"crossref","unstructured":"Perlin, K.: Association for computing machinery. In: SIGGRAPH (vol. 2, p. 681) (2002)","DOI":"10.1145\/566654.566636"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Ristea, N.C., et al.: Self-supervised predictive convolutional attentive block for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13576\u201313586 (2022)","DOI":"10.1109\/CVPR52688.2022.01321"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"37_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"37_CR20","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)"},{"issue":"2","key":"37_CR21","first-page":"651","volume":"2","author":"D Kermany","year":"2018","unstructured":"Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography (OCT) and chest X-ray images for classification. Mendeley Data 2(2), 651 (2018)","journal-title":"Mendeley Data"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"37_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45170-6_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:01:22Z","timestamp":1730505682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45170-6_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031451690","9783031451706"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45170-6_37","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":"4 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isical.ac.in\/~premi23\/","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"311","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":"91","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":"29% - 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)"}}]}}