99,99 €
Big Data in Multimodal Medical Imaging
Big Data in Multimodal Medical Imaging
  • Išparduota
Big Data in Multimodal Medical Imaging
Big Data in Multimodal Medical Imaging
El. knyga:
99,99 €
There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis an…
  • Leidėjas:
  • Metai: 2019
  • Puslapiai: 330
  • ISBN: 9781351380737
  • ISBN-10: 1351380737
  • ISBN-13: 9781351380737
  • Formatas: ACSM ?
  • Kalba: Anglų

Big Data in Multimodal Medical Imaging (el. knyga) (skaityta knyga) | knygos.lt

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There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

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  • Leidėjas:
  • Metai: 2019
  • Puslapiai: 330
  • ISBN: 9781351380737
  • ISBN-10: 1351380737
  • ISBN-13: 9781351380737
  • Formatas: ACSM ?
  • Kalba: Anglų

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

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