{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,29]],"date-time":"2025-06-29T15:58:21Z","timestamp":1751212701510,"version":"3.37.3"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2019YJ0176","2019YJ0177","2019YFQ0005"],"award-info":[{"award-number":["2019YJ0176","2019YJ0177","2019YFQ0005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE MultiMedia"],"published-print":{"date-parts":[[2022,4,1]]},"DOI":"10.1109\/mmul.2022.3142986","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T20:28:09Z","timestamp":1642105689000},"page":"114-123","source":"Crossref","is-referenced-by-count":5,"title":["FLeak-Seg: Automated Fundus Fluorescein Leakage Segmentation via Cross-Modal Attention Learning"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0561-6229","authenticated-orcid":false,"given":"Yang","family":"Wen","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Leiting","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Lifeng","family":"Qiao","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Yu","family":"Deng","sequence":"additional","affiliation":[{"name":"King&#x2019;s College London, London, U.K."}]},{"given":"Haisheng","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Tian","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7700-7188","authenticated-orcid":false,"given":"Chuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00448"},{"key":"ref11","first-page":"1","article-title":"Let&#x2019;s find fluorescein: Cross-modal dual attention learning for fluorescein leakage segmentation in fundus fluorescein angiography","author":"wen","year":"2021","journal-title":"Proc IEEE Int Conf Multimedia Expo"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1006\/cbmr.1998.1487"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2008.4650444"},{"key":"ref14","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Interv"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"ref16","first-page":"801","article-title":"Encoder-decoder with Atrous separable convolution for semantic image segmentation","author":"chen","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3116265"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2014.2854"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1097\/IAE.0000000000002045"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/srep10425"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/BF00170690"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2593725"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.14-15457"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2017.01.003"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"ursula","year":"2018","journal-title":"Prog Retinal Eye Res"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00391"}],"container-title":["IEEE MultiMedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/93\/9830637\/09681186.pdf?arnumber=9681186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T19:10:19Z","timestamp":1688757019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9681186\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":20,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/mmul.2022.3142986","relation":{},"ISSN":["1070-986X","1941-0166"],"issn-type":[{"type":"print","value":"1070-986X"},{"type":"electronic","value":"1941-0166"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}