{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:55:34Z","timestamp":1780084534351,"version":"3.54.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031185755","type":"print"},{"value":"9783031185762","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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18576-2_2","type":"book-chapter","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:04:21Z","timestamp":1665151461000},"page":"14-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Cross Attention Transformers for\u00a0Multi-modal Unsupervised Whole-Body PET Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-2578","authenticated-orcid":false,"given":"Ashay","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6435-5079","authenticated-orcid":false,"given":"Petru-Daniel","family":"Tudosiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3739-1087","authenticated-orcid":false,"given":"Walter Hugo Lopez","family":"Pinaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-8134","authenticated-orcid":false,"given":"Gary","family":"Cook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-8091","authenticated-orcid":false,"given":"Vicky","family":"Goh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-5340","authenticated-orcid":false,"given":"Sebastien","family":"Ourselin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-2558","authenticated-orcid":false,"given":"M. Jorge","family":"Cardoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4103\/0256-4947.75771","volume":"31","author":"A Almuhaideb","year":"2011","unstructured":"Almuhaideb, A., Papathanasiou, N., Bomanji, J.: 18F-FDG PET\/CT imaging in oncology. Ann. Saudi Med. 31, 3\u201313 (2011). https:\/\/doi.org\/10.4103\/0256-4947.75771","journal-title":"Ann. Saudi Med."},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Baur, C., Denner, S., Wiestler, B., Albarqouni, S., Navab, N.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study (2020)","DOI":"10.1016\/j.media.2020.101952"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Burgos, N., et al.: Anomaly detection for the individual analysis of brain pet images. J. Med. Imaging (Bellingham, Wash.) 8, 024003 (2021). https:\/\/doi.org\/10.1117\/1.JMI.8.2.024003","DOI":"10.1117\/1.JMI.8.2.024003"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C.F., Fan, Q., Panda, R.: CrossViT: cross-attention multi-scale vision transformer for image classification (2021)","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"2_CR5","unstructured":"Chen, M., Radford, A., Wu, J., Heewoo, J., Dhariwal, P.: Generative pretraining from pixels (2020)"},{"key":"2_CR6","unstructured":"Child, R., Gray, S., Radford, A., Sutskever, I.: Generating long sequences with sparse transformers (2019)"},{"key":"2_CR7","unstructured":"Choromanski, K., et al.: Rethinking attention with performers (2020)"},{"key":"2_CR8","unstructured":"Dhariwal, P., Jun, H., Payne, C., Kim, J.W., Radford, A., Sutskever, I.: Jukebox: a generative model for music (2020)"},{"key":"2_CR9","unstructured":"Dumoulin, V., et al.: Adversarially learned inference (2016)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis (2020)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Gheini, M., Ren, X., May, J.: Cross-attention is all you need: Adapting pretrained transformers for machine translation (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.132"},{"key":"2_CR12","unstructured":"Jun, H., Child, R., Chen, M., Schulman, J.: Distribution augmentation for generative modeling (2020)"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Kim, H.S., Lee, K.S., Ohno, Y., van Beek, E.J.R., Biederer, J.: PET\/CT versus mri for diagnosis, staging, and follow-up of lung cancer. J. Magn. Reson. Imaging: JMRI 42, 247\u201360 (2015). https:\/\/doi.org\/10.1002\/jmri.24776","DOI":"10.1002\/jmri.24776"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1159\/000456006","volume":"82","author":"B Liu","year":"2017","unstructured":"Liu, B., Gao, S., Li, S.: A comprehensive comparison of CT, MRI, positron emission tomography or positron emission tomography\/CT, and diffusion weighted imaging-MRI for detecting the lymph nodes metastases in patients with cervical cancer: A meta-analysis based on 67 studies. Gynecol. Obstet. Invest. 82, 209\u2013222 (2017). https:\/\/doi.org\/10.1159\/000456006","journal-title":"Gynecol. Obstet. Invest."},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Marimont, S.N., Tarroni, G.: Anomaly detection through latent space restoration using vector-quantized variational autoencoders (2020)","DOI":"10.1109\/ISBI48211.2021.9433778"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Mohla, S., Pande, S., Banerjee, B., Chaudhuri, S.: FusAtNet: dual attention based spectrospatial multimodal fusion network for hyperspectral and lidar classification, pp. 416\u2013425. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00054","DOI":"10.1109\/CVPRW50498.2020.00054"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Newman-Toker, D.E., et al.: Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the \u201cbig three\u201d. Diagnosis (Berlin, Germany) 8, 67\u201384 (2021). https:\/\/doi.org\/10.1515\/dx-2019-0104","DOI":"10.1515\/dx-2019-0104"},{"key":"2_CR18","unstructured":"van den Oord, A., Vinyals, O., Kavukcuoglu, K.: Neural discrete representation learning (2017)"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065\u20131076 (1962). https:\/\/doi.org\/10.1214\/aoms\/1177704472","journal-title":"Ann. Math. Stat."},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Perani, D., et al.: A survey of FDG- and amyloid-pet imaging in dementia and grade analysis. Biomed. Res. Int. 2014, 785039 (2014). https:\/\/doi.org\/10.1155\/2014\/785039","DOI":"10.1155\/2014\/785039"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Unsupervised brain anomaly detection and segmentation with transformers (2021)","DOI":"10.1016\/j.media.2022.102475"},{"key":"2_CR22","unstructured":"Radford, A., Narasimhan, K.: Improving language understanding by generative pre-training (2018)"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Takaki, S., Nakashika, T., Wang, X., Yamagishi, J.: STFT spectral loss for training a neural speech waveform model (2018)","DOI":"10.1109\/ICASSP.2019.8683791"},{"key":"2_CR24","unstructured":"Tay, Y., et al.: Long range arena: a benchmark for efficient transformers (2020)"},{"key":"2_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need (2017)"},{"key":"2_CR26","unstructured":"Yan, W., Zhang, Y., Abbeel, P., Srinivas, A.: VideoGPT: Video generation using VQ-VAE and transformers (2021)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Deep Generative Models"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18576-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:05:22Z","timestamp":1665151522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18576-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031185755","9783031185762"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18576-2_2","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":"8 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGM4MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Deep Generative Models","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":"22 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":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dgm4miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgm4miccai.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":"15","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":"12","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":"80% - 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)"}}]}}