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Machine Learning in Clinical Neuroimaging

Om Machine Learning in Clinical Neuroimaging

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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  • Språk:
  • Engelska
  • ISBN:
  • 9783031448577
  • Format:
  • Häftad
  • Sidor:
  • 184
  • Utgiven:
  • 8. oktober 2023
  • Utgåva:
  • 23001
  • Mått:
  • 155x11x235 mm.
  • Vikt:
  • 289 g.
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Leveranstid: 2-4 veckor
Förväntad leverans: 18. mars 2025

Beskrivning av Machine Learning in Clinical Neuroimaging

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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