Myopic Maculopathy Analysis MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8–12, 2023, Proceedings /
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 presen...
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| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | Αγγλικά |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Έκδοση: | 1st ed. 2024. |
| Σειρά: | Lecture Notes in Computer Science,
14563 |
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| Διαθέσιμο Online: | https://doi.org/10.1007/978-3-031-54857-4 |
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| 245 | 1 | 0 | |a Myopic Maculopathy Analysis |h [electronic resource] : |b MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8–12, 2023, Proceedings / |c edited by Bin Sheng, Hao Chen, Tien Yin Wong. |
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| 505 | 0 | |a 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 | |a 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. | ||
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