{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T03:15:07Z","timestamp":1788491707027,"version":"build-2803163510"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100031334","name":"Endometriosis Australia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031334","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018178","name":"Australasian Society for Ultrasound in Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018178","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011025","name":"Government of Western Australia","doi-asserted-by":"publisher","award":["Medical Research Futures Fund: Primary Health Care Research Data Infrastructure Grant 2020"],"award-info":[{"award-number":["Medical Research Futures Fund: Primary Health Care Research Data Infrastructure Grant 2020"]}],"id":[{"id":"10.13039\/501100011025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.artmed.2026.103503","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T15:09:37Z","timestamp":1786374577000},"page":"103503","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Unpaired multi-modal multi-label learning for detecting endometriosis signs"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-3140","authenticated-orcid":false,"given":"Yuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh-Son","family":"To","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Knox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5538-6906","authenticated-orcid":false,"given":"Mathew","family":"Leonardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Condous","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8857-9162","authenticated-orcid":false,"given":"Jodie C.","family":"Avery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-3971","authenticated-orcid":false,"given":"M. Louise","family":"Hull","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5571-6220","authenticated-orcid":false,"given":"Gustavo","family":"Carneiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10276","key":"10.1016\/j.artmed.2026.103503_b1","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/S0140-6736(21)00389-5","article-title":"Endometriosis is a chronic systemic disease: clinical challenges and novel innovations","volume":"397","author":"Taylor","year":"2021","journal-title":"Lancet"},{"issue":"4","key":"10.1016\/j.artmed.2026.103503_b2","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1111\/1471-0528.16447","article-title":"Prevalence and incidence of endometriosis in Australian women: a data linkage cohort study","volume":"128","author":"Rowlands","year":"2021","journal-title":"BJOG: Int J Obs Gynaecol"},{"issue":"10","key":"10.1016\/j.artmed.2026.103503_b3","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0223316","article-title":"The cost of illness and economic burden of endometriosis and chronic pelvic pain in Australia: a national online survey","volume":"14","author":"Armour","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.artmed.2026.103503_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12905-021-01545-5","article-title":"Treatment of endometriosis: a review with comparison of 8 guidelines","volume":"21","author":"Kalaitzopoulos","year":"2021","journal-title":"BMC Womens Health"},{"issue":"2","key":"10.1016\/j.artmed.2026.103503_b5","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.fertnstert.2011.05.090","article-title":"Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries","volume":"96","author":"Nnoaham","year":"2011","journal-title":"Fertil Steril"},{"issue":"2","key":"10.1016\/j.artmed.2026.103503_b6","doi-asserted-by":"crossref","first-page":"hoac009","DOI":"10.1093\/hropen\/hoac009","article-title":"ESHRE guideline: endometriosis","volume":"2022","author":"Becker","year":"2022","journal-title":"Hum Reprod Open"},{"issue":"6","key":"10.1016\/j.artmed.2026.103503_b7","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1111\/1754-9485.12680","article-title":"MRI findings in deep infiltrating endometriosis: A pictorial essay","volume":"61","author":"Thalluri","year":"2017","journal-title":"J Med Imaging Radiat Oncol"},{"issue":"3","key":"10.1016\/j.artmed.2026.103503_b8","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1002\/uog.15955","article-title":"Systematic approach to sonographic evaluation of the pelvis in women with suspected endometriosis, including terms, definitions and measurements: a consensus opinion from the International Deep Endometriosis Analysis (IDEA) group","volume":"48","author":"Guerriero","year":"2016","journal-title":"Ultrasound Obs Gynecol"},{"key":"10.1016\/j.artmed.2026.103503_b9","series-title":"2023 IEEE 20th international symposium on biomedical imaging","first-page":"1","article-title":"Distilling missing modality knowledge from ultrasound for endometriosis diagnosis with magnetic resonance images","author":"Zhang","year":"2023"},{"issue":"7","key":"10.1016\/j.artmed.2026.103503_b10","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.3390\/diagnostics12071767","article-title":"Transvaginal ultrasound vs. magnetic resonance imaging (MRI) value in endometriosis diagnosis","volume":"12","author":"Bau\u015fic","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.artmed.2026.103503_b11","series-title":"European conference on computer vision","first-page":"200","article-title":"Uncertainty-aware multi-modal learning via cross-modal random network prediction","author":"Wang","year":"2022"},{"key":"10.1016\/j.artmed.2026.103503_b12","doi-asserted-by":"crossref","first-page":"33536","DOI":"10.52202\/068431-2430","article-title":"Multi-granularity cross-modal alignment for generalized medical visual representation learning","volume":"35","author":"Wang","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"10.1016\/j.artmed.2026.103503_b13","article-title":"Imaging modalities for the non-invasive diagnosis of endometriosis","author":"Nisenblat","year":"2016","journal-title":"Cochrane Database Syst Rev"},{"issue":"6","key":"10.1016\/j.artmed.2026.103503_b14","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/uog.12305","article-title":"Prediction of pouch of Douglas obliteration in women with suspected endometriosis using a new real-time dynamic transvaginal ultrasound technique: the sliding sign","volume":"41","author":"Reid","year":"2013","journal-title":"Ultrasound Obs Gynecol"},{"key":"10.1016\/j.artmed.2026.103503_b15","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2147\/IJWH.S190326","article-title":"Bowel endometriosis: current perspectives on diagnosis and treatment","author":"Habib","year":"2020","journal-title":"Int J Women\u2019s Health"},{"issue":"3","key":"10.1016\/j.artmed.2026.103503_b16","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jmig.2011.01.011","article-title":"Is pouch of Douglas obliteration a marker of bowel endometriosis?","volume":"18","author":"Khong","year":"2011","journal-title":"J Minim Invasive Gynecol"},{"key":"10.1016\/j.artmed.2026.103503_b17","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"216","article-title":"Learnable cross-modal knowledge distillation for multi-modal learning with missing modality","author":"Wang","year":"2023"},{"key":"10.1016\/j.artmed.2026.103503_b18","series-title":"2025 IEEE\/CVF winter conference on applications of computer vision","first-page":"3549","article-title":"A novel perspective for multi-modal multi-label skin lesion classification","author":"Zhang","year":"2025"},{"key":"10.1016\/j.artmed.2026.103503_b19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ejogrb.2021.04.012","article-title":"Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis","volume":"261","author":"Guerriero","year":"2021","journal-title":"Eur J Obs Gynecol Reprod Biol"},{"issue":"4","key":"10.1016\/j.artmed.2026.103503_b20","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1530\/RAF-21-0031","article-title":"Deep learning to diagnose pouch of douglas obliteration with ultrasound sliding sign","volume":"2","author":"Maicas","year":"2021","journal-title":"Reprod Fertil"},{"issue":"7","key":"10.1016\/j.artmed.2026.103503_b21","doi-asserted-by":"crossref","first-page":"7598","DOI":"10.1007\/s11227-020-03535-0","article-title":"Diagnostic efficacy of ultrasound combined with magnetic resonance imaging in diagnosis of deep pelvic endometriosis under deep learning","volume":"77","author":"Yang","year":"2021","journal-title":"J Supercomput"},{"key":"10.1016\/j.artmed.2026.103503_b22","series-title":"Medical imaging 2023: ultrasonic imaging and tomography","first-page":"118","article-title":"Augmenting endometriosis analysis from ultrasound data using deep learning","volume":"vol. 12470","author":"Balica","year":"2023"},{"key":"10.1016\/j.artmed.2026.103503_b23","series-title":"2023 45th annual international conference of the IEEE engineering in medicine & biology society","first-page":"1","article-title":"The effectiveness of self-supervised pre-training for multi-modal endometriosis classification","author":"Butler","year":"2023"},{"key":"10.1016\/j.artmed.2026.103503_b24","article-title":"Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis","author":"Zhang","year":"2025","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.artmed.2026.103503_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103214","article-title":"Confidence-aware multi-modality learning for eye disease screening","volume":"96","author":"Zou","year":"2024","journal-title":"Med Image Anal"},{"issue":"7","key":"10.1016\/j.artmed.2026.103503_b26","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1109\/TMI.2019.2963882","article-title":"Unpaired multi-modal segmentation via knowledge distillation","volume":"39","author":"Dou","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"11","key":"10.1016\/j.artmed.2026.103503_b27","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun ACM"},{"key":"10.1016\/j.artmed.2026.103503_b28","series-title":"Medical imaging with deep learning","first-page":"1006","article-title":"Incomplete learning of multi-modal connectome for brain disorder diagnosis via modal-mixup and deep supervision","author":"Yang","year":"2024"},{"key":"10.1016\/j.artmed.2026.103503_b29","series-title":"MIDL 2024-medical imaging with deep learning","article-title":"ADAPT: Multimodal learning for detecting physiological changes under missing modalities","author":"Mordacq","year":"2024"},{"key":"10.1016\/j.artmed.2026.103503_b30","doi-asserted-by":"crossref","unstructured":"Li S, Chen C, Han J. SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2025, p. 24068\u201377.","DOI":"10.1109\/ICCV51701.2025.02231"},{"key":"10.1016\/j.artmed.2026.103503_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106791","article-title":"MedViT: a robust vision transformer for generalized medical image classification","volume":"157","author":"Manzari","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.artmed.2026.103503_b32","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"376","article-title":"Self-ensembling vision transformer (sevit) for robust medical image classification","author":"Almalik","year":"2022"},{"key":"10.1016\/j.artmed.2026.103503_b33","series-title":"Medslip: Medical dual-stream language-image pre-training for fine-grained alignment","author":"Fan","year":"2024"},{"issue":"7","key":"10.1016\/j.artmed.2026.103503_b34","doi-asserted-by":"crossref","first-page":"981","DOI":"10.2967\/jnumed.121.262239","article-title":"Alzheimer disease: standard of diagnosis, treatment, care, and prevention","volume":"63","author":"Teipel","year":"2022","journal-title":"J Nucl Med"},{"issue":"2","key":"10.1016\/j.artmed.2026.103503_b35","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","article-title":"Seven-point checklist and skin lesion classification using multitask multimodal neural nets","volume":"23","author":"Kawahara","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.artmed.2026.103503_b36","doi-asserted-by":"crossref","unstructured":"He K, Chen X, Xie S, Li Y, Doll\u00e1r P, Girshick R. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 16000\u20139.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.artmed.2026.103503_b37","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.artmed.2026.103503_b38","doi-asserted-by":"crossref","unstructured":"Kobayashi T. Two-way multi-label loss. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 7476\u201385.","DOI":"10.1109\/CVPR52729.2023.00722"},{"key":"10.1016\/j.artmed.2026.103503_b39","doi-asserted-by":"crossref","DOI":"10.18637\/jss.v086.i08","article-title":"Image segmentation, registration and characterization in R with SimpleITK","volume":"86","author":"Beare","year":"2018","journal-title":"J Stat Softw"},{"key":"10.1016\/j.artmed.2026.103503_b40","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fninf.2013.00045","article-title":"The design of SimpleITK","volume":"7","author":"Lowekamp","year":"2013","journal-title":"Front Neuroinformatics"},{"issue":"3","key":"10.1016\/j.artmed.2026.103503_b41","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/s10278-017-0037-8","article-title":"SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research","volume":"31","author":"Yaniv","year":"2018","journal-title":"J Digit Imaging"},{"issue":"3","key":"10.1016\/j.artmed.2026.103503_b42","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.artmed.2026.103503_b43","doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M. A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 6450\u20139.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"10.1016\/j.artmed.2026.103503_b44","series-title":"The kinetics human action video dataset","author":"Kay","year":"2017"},{"key":"10.1016\/j.artmed.2026.103503_b45","doi-asserted-by":"crossref","first-page":"10078","DOI":"10.52202\/068431-0732","article-title":"Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training","volume":"35","author":"Tong","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.artmed.2026.103503_b46","doi-asserted-by":"crossref","unstructured":"Wang W, Ding L, Shen L, Luo Y, Hu H, Tao D. Wisdom: Improving multimodal sentiment analysis by fusing contextual world knowledge. In: Proceedings of the 32nd ACM international conference on multimedia. 2024, p. 2282\u201391.","DOI":"10.1145\/3664647.3681403"},{"key":"10.1016\/j.artmed.2026.103503_b47","doi-asserted-by":"crossref","unstructured":"Wang W, Ding L, Zeng M, Zhou X, Shen L, Luo Y, Yu W, Tao D. Divide, conquer and combine: A training-free framework for high-resolution image perception in multimodal large language models. In: Proceedings of the AAAI conference on artificial intelligence. vol. 39, 2025, p. 7907\u201315, 8.","DOI":"10.1609\/aaai.v39i8.32852"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365726001557?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365726001557?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T02:51:53Z","timestamp":1788490313000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365726001557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":47,"alternative-id":["S0933365726001557"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2026.103503","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unpaired multi-modal multi-label learning for detecting endometriosis signs","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2026.103503","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103503"}}