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Titlebook: Machine Learning in Clinical Neuroimaging; 5th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Thomas Wolfers Conference proceedings 20

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书目名称Machine Learning in Clinical Neuroimaging
副标题5th International Wo
编辑Ahmed Abdulkadir,Deepti R. Bathula,Thomas Wolfers
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning in Clinical Neuroimaging; 5th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Thomas Wolfers Conference proceedings 20
描述This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. .The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions..The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track)..The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration. .
出版日期Conference proceedings 2022
关键词artificial intelligence; bioinformatics; computer networks; computer science; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-031-17899-3
isbn_softcover978-3-031-17898-6
isbn_ebook978-3-031-17899-3Series 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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Machine Learning in Clinical Neuroimaging978-3-031-17899-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620660.jpg
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Joint Reconstruction and Parcellation of Cortical Surfacesanch and another based on a novel generic 3D reconstruction loss, to augment template-deformation algorithms such that the surface meshes directly come with an atlas-based brain parcellation. By combining both options with two of the latest cortical surface reconstruction algorithms, we attain highl
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A Study of Demographic Bias in CNN-Based Brain MR Segmentationance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for
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