Deep Generative Models (Record no. 87507)

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control field 978-3-031-53767-7
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control field 20240730171320.0
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031537677
-- 978-3-031-53767-7
082 04 - CLASSIFICATION NUMBER
Call Number 006.37
245 10 - TITLE STATEMENT
Title Deep Generative Models
Sub Title Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings /
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Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XI, 248 p. 86 illus., 76 illus. in color.
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Series statement Lecture Notes in Computer Science,
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Remark 2 Methods -- Applications -- Methodology. Causal inference. Latent interpretation. Generative factor analysis -- Mammography. Vessel imaging -- Surgical Videos.
520 ## - SUMMARY, ETC.
Summary, etc This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions. The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgical Videos.
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General subdivision Data processing.
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Author 2 Mukhopadhyay, Anirban.
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Author 2 Oksuz, Ilkay.
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Author 2 Engelhardt, Sandy.
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Author 2 Zhu, Dajiang.
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Author 2 Yuan, Yixuan.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-53767-7
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Koha item type eBooks
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-- Springer Nature Switzerland :
-- Imprint: Springer,
-- 2024.
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-- Computer vision.
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-- Machine learning.
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-- Education
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-- Application software.
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-- Computer Vision.
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-- Machine Learning.
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-- Computers and Education.
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-- Computer and Information Systems Applications.
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