Multimodality Imaging, Volume 1
- Indbinding:
- Hardback
- Sideantal:
- 356
- Udgivet:
- 20. december 2022
- Størrelse:
- 178x254x27 mm.
- 2-3 uger.
- 22. januar 2025
På lager
Forlænget returret til d. 31. januar 2025
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- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Multimodality Imaging, Volume 1
This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively.This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging. Key Features: Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classificationExplores imaging applications, their complexities and the Deep Learning models employed to resolve them in detailProvides state-of-the-art contributions while addressing doubts in multimodal researchDetails the future of deep learning and big data in medical imaging
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