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portada Translational Deep Learning in Medical Imaging. Theoretical Foundations and Clinical Applications
Type
Physical Book
Publisher
Author
Year
2026
Language
English
Pages
360
Format
Hardcover
Dimensions
22.9x15.2 cm
ISBN13
9789372198591

Translational Deep Learning in Medical Imaging. Theoretical Foundations and Clinical Applications

C. Kishor Kumar Reddy;Shugufta Fatima;P. R. Anisha;Hasyiya Karimah Adli;Anitha Veeramalla (Author) · New India Publishing Agency – NIPA · Hardcover

Translational Deep Learning in Medical Imaging. Theoretical Foundations and Clinical Applications - C. Kishor Kumar Reddy;Shugufta Fatima;P. R. Anisha;Hasyiya Karimah Adli;Anitha Veeramalla

New Book Imported to New Zealand
Delivery: 19 Jan - 29 Jan Shipping: 42 to 46 business days.
NZ$ 672.38
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Synopsis "Translational Deep Learning in Medical Imaging. Theoretical Foundations and Clinical Applications"

This book, Translational Deep Learning in Medical Imaging: Theoretical Foundations and Clinical Applications, explores how advanced deep learning methodologies are transforming the landscape of medical imaging from theoretical innovation to real-world clinical practice. Moving beyond conventional image analysis and rule-based diagnostic systems, the book investigates how convolutional neural networks, transformer architectures, and hybrid AI models enable accurate detection, segmentation, and prognosis across diverse medical conditions.

It covers key themes such as image classification, multimodal data integration, explainable AI, radiomics, and real-time diagnostic support systems, with a strong emphasis on bridging the gap between laboratory research and clinical deployment. Through practical case studies and validated clinical workflows, the book demonstrates how deep learning enhances diagnostic precision, workflow efficiency, and patient outcomes. It also addresses critical challenges including data heterogeneity, model interpretability, regulatory compliance, and ethical considerations, while outlining future directions for scalable, trustworthy, and clinically adaptable AI-driven imaging solutions.

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