03303nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001300172050001400185072001600199072002300215072001500238245022200253250001800475264006900493300006300562336002600625337002600651338003600677347002400713490005900737505095400796520032601750650003602076650002102112650003602133650002102169700014702190700007902337700008402416710003402500773002602534776003602560776003602596830005902632856004602691912001402737912001502751912001402766999001702780978-3-031-54857-4DE-He21320251030111451.0cr nn 008mamaa240228s2024    sz |    s    |||| 0|eng d  a97830315485749978-3-031-54857-47 a10.1007/978-3-031-54857-42doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema10aMyopic Maculopathy Analysish[electronic resource] :bMICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8–12, 2023, Proceedings /cedited by Bin Sheng, Hao Chen, Tien Yin Wong.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aX, 121 p. 33 illus., 31 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Computer Science,x1611-3349 ;v145630 aAutomated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction -- Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus Images -- Towards Label-efficient Deep Learning for Myopic Maculopathy Classification -- Ensemble Deep Learning Approaches for Myopic Maculopathy Plus Lesions Segmentation -- Beyond MobileNet: An improved MobileNet for Retinal Diseases -- Prediction of Spherical Equivalent With Vanilla ResNet -- Semi-supervised learning for Myopic Maculopathy Analysis -- A Clinically Guided Approach for Training Deep Neural Networks for Myopic Maculopathy Classification -- Classification of Myopic Maculopathy Images with Self-supervised Driven Multiple Instance Learning Network -- Self-supervised Learning and Data Diversity based Prediction of Spherical Equivalent -- Myopic Maculopathy Analysis using Multi-Task Learning and Pseudo Labeling.  aThis book constitutes the MICCAI Challenge, MMAC 2023, that held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, which took place in October 2023. The 11 long papers included in this volume presents a wide range of state-of-the-art deep learning methods developed for the various tasks presented in the challenge. 0aArtificial intelligence.958441 0aComputer vision.14aArtificial Intelligence.95844124aComputer Vision.1 aSheng, Bin.eeditor.0(orcid)0000-0001-8678-27841https://orcid.org/0000-0001-8678-27844edt4http://id.loc.gov/vocabulary/relators/edt9684381 aChen, Hao.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9720011 aWong, Tien Yin.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9720022 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303154856708iPrinted edition:z9783031548581 0aLecture Notes in Computer Science,x1611-3349 ;v1456340uhttps://doi.org/10.1007/978-3-031-54857-4  aZDB-2-SCS  aZDB-2-SXCS  aZDB-2-LNC  c57970d57970