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Medical Imaging: Applications of Deep Learning

Bag om Medical Imaging: Applications of Deep Learning

Medical imaging refers to several different technologies that are used to view inside the human body in order to diagnose, monitor or treat medical conditions. Endoscopy, tactile imaging and computerized tomography (CT) scan are some common types of medical imaging procedures. In recent years, deep learning or deep structured imaging has found applications in the field of medical image analysis. Machine learning is a subfield of artificial intelligence (AI) and is used for various purposes such as computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. AI tools are used to accelerate productivity and potentially improve accuracy in the process of medical imaging. This book contains some path-breaking studies in the field of deep learning and medical imaging. The various studies that are constantly contributing towards advancing technologies and evolution of this field are examined in detail. It will serve as a valuable source of reference for medical students, radiologists, and industry researchers engaged in medical imaging research.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781646465644
  • Indbinding:
  • Hardback
  • Sideantal:
  • 249
  • Udgivet:
  • 26. september 2023
  • Størrelse:
  • 216x16x279 mm.
  • Vægt:
  • 871 g.
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Leveringstid: 2-3 uger
Forventet levering: 22. januar 2025

Beskrivelse af Medical Imaging: Applications of Deep Learning

Medical imaging refers to several different technologies that are used to view inside the human body in order to diagnose, monitor or treat medical conditions. Endoscopy, tactile imaging and computerized tomography (CT) scan are some common types of medical imaging procedures. In recent years, deep learning or deep structured imaging has found applications in the field of medical image analysis. Machine learning is a subfield of artificial intelligence (AI) and is used for various purposes such as computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. AI tools are used to accelerate productivity and potentially improve accuracy in the process of medical imaging. This book contains some path-breaking studies in the field of deep learning and medical imaging. The various studies that are constantly contributing towards advancing technologies and evolution of this field are examined in detail. It will serve as a valuable source of reference for medical students, radiologists, and industry researchers engaged in medical imaging research.

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