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Titlebook: Medical Image Understanding and Analysis; 28th Annual Conferen Moi Hoon Yap,Connah Kendrick,Reyer Zwiggelaar Conference proceedings 2024 Th

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書目名稱Medical Image Understanding and Analysis
副標(biāo)題28th Annual Conferen
編輯Moi Hoon Yap,Connah Kendrick,Reyer Zwiggelaar
視頻videohttp://file.papertrans.cn/630/629283/629283.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Medical Image Understanding and Analysis; 28th Annual Conferen Moi Hoon Yap,Connah Kendrick,Reyer Zwiggelaar Conference proceedings 2024 Th
描述.This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 24–26, 2024...The 59 full papers?included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows:?..Part I :?Advancement in Brain Imaging;?Medical Images and Computational Models; and?Digital Pathology, Histology and Microscopic Imaging...Part II :?Dental and Bone Imaging;?Enhancing Low-Quality Medical Images; Domain Adaptation and Generalisation; and Dermatology, Cardiac Imaging and Other Medical Imaging..
出版日期Conference proceedings 2024
關(guān)鍵詞Medical image Analysis; Brain Imaging; Digital Pathology; Computational Models; Microscopic Imaging; AI
版次1
doihttps://doi.org/10.1007/978-3-031-66955-2
isbn_softcover978-3-031-66954-5
isbn_ebook978-3-031-66955-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Medical Image Understanding and Analysis978-3-031-66955-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Learning-Based MRI Response Predictions from?OCT Microvascular Models to?Replace Simulation-Based Fr response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) <1%, R. of 82.8% and explained variance score of 82.9%.
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