株式会社極東書店トップ ＞ 商品一覧 ＞ Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. 1st ed. 2023
商品詳細
Machine Learning in Clinical Neuroimaging: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. 1st ed. 2023
・ISBN 978-3-031-44857-7 paper EUR 49.99
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| 著者・編者 | Abdulkadir, Ahmed / Bathula, Deepti R. / Dvornek, Nicha C. / Govindarajan, Sindhuja T. / Habes, Mohamad / Kumar, Vinod / Leonardsen, Esten / Wolfers, Thomas / Xiao, Yiming (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2023 |
| ページ数 | 174 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A01-82022＞ |
解説
This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023.
The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions.
The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) 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 on Machine Learning and Clinical Applications.
The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions.
The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) 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 on Machine Learning and Clinical Applications.