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Artificial Intelligence in Diagnostics and Imaging Technologies in Healthcare: In honour of Professor Dr. George A. Tsihrintzis for his Invaluable Contributions [Hardback]

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  • Formāts: Hardback, 242 pages, height x width: 235x155 mm, 85 Illustrations, color; 10 Illustrations, black and white; X, 242 p. 95 illus., 85 illus. in color., 1 Hardback
  • Sērija : Intelligent Systems Reference Library 281
  • Izdošanas datums: 14-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031981480
  • ISBN-13: 9783031981487
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  • Hardback
  • Cena: 180,78 €*
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Artificial Intelligence in Diagnostics and Imaging Technologies in Healthcare: In honour of Professor Dr. George A. Tsihrintzis for his Invaluable ContributionsPalielināt
  • Formāts: Hardback, 242 pages, height x width: 235x155 mm, 85 Illustrations, color; 10 Illustrations, black and white; X, 242 p. 95 illus., 85 illus. in color., 1 Hardback
  • Sērija : Intelligent Systems Reference Library 281
  • Izdošanas datums: 14-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031981480
  • ISBN-13: 9783031981487
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031981487.html

This book brings together cutting-edge research and innovative applications of artificial intelligence, machine learning, wearable technologies and decision support systems in modern healthcare. The chapters demonstrate how these artificial intelligence innovations are driving next-generation healthcare solutions. The chapters present diverse clinical applications through advanced artificial intelligence approaches in diagnostics, treatment, rehabilitation and healthcare management, with a strong emphasis on patient-centred solutions. This book is directed to students, professors, researchers, practitioners in the field of healthcare, engineering, computer science and related areas.

Self-supervised and disease management system for diabetic retinopathy
detection.- GAN-enhanced Abdominal MR-CT Image Fusion in Transform Domain.-
U-Net with Attention for the Automatic Segmentation of the Major Temporal
Arcade in Retinal Fundus Images.- Harnessing Few-Shot Learning Segmentation
for Histopathology: A Comprehensive Practical Study.- Healthcare Applications
of Object Detection for Tumor Detection Across Diverse Medical Imaging.-
Automatic Multilabel Classification of Coronary Stenosis using Lightweight
Deep 
Learning Techniques.