Myopic Maculopathy Analysis (Record no. 87577)

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fixed length control field 03423nam a22005535i 4500
001 - CONTROL NUMBER
control field 978-3-031-54857-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730171428.0
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fixed length control field 240228s2024 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031548574
-- 978-3-031-54857-4
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
245 10 - TITLE STATEMENT
Title Myopic Maculopathy Analysis
Sub Title MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages X, 121 p. 33 illus., 31 illus. in color.
490 1# - SERIES STATEMENT
Series statement Lecture Notes in Computer Science,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Automated 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.
520 ## - SUMMARY, ETC.
Summary, etc This 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.
700 1# - AUTHOR 2
Author 2 Sheng, Bin.
700 1# - AUTHOR 2
Author 2 Chen, Hao.
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Author 2 Wong, Tien Yin.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-54857-4
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Koha item type eBooks
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-- Springer Nature Switzerland :
-- Imprint: Springer,
-- 2024.
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer vision.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Vision.
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-- 1611-3349 ;
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