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Machine Learning in Clinical Neuroimaging edited by Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Sindhuja T. Govindarajan, Mohamad Habes, Vinod Kumar, Esten Leonardsen, Thomas Wolfers, Yiming Xiao. [electronic resource] :

Contributor(s): Material type: TextTextPublication details: Cham Springer Nature Switzerland 2023Edition: 1st ed. 2023Description: X, 174 p. 52 illus., 47 illus. in color. online resourceISBN:
  • 9783031448584
Subject(s): DDC classification:
  • 6.37
Online resources: Summary: 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.
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Item type Home library Call number Status Notes Date due Barcode Item holds
e-Book e-Book S. R. Ranganathan Learning Hub Online Available Platform:Springer EB2755
Total holds: 0

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.

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