Keywords
Medical Imaging, Deep Learning, Artificial Intelligence in Healthcare, Clinical AI, Medical Image Analysis, Radiology AI, Computer-Aided Diagnosis, Deep Learning in Medicine, Biomedical Imaging, Machine Learning in Healthcare, Diagnostic Imaging, Clinical Decision Support Systems, Convolutional Neural Networks, Explainable AI, Precision Medicine, Transfer Learning, Medical Image Segmentation, Healthcare Informatics, AI in Radiology, Translational MedicineTranslational Deep Learning in Medical Imaging: Theoretical Foundations and Clinical Applications
edited by: C. Kishor Kumar Reddy, Shugufta Fatima, P. R. Anisha, Hasyiya Karimah Adli & Anitha VeeramallaLength: 229 mm | Breadth: 14.36 mm | Height: 152 mm | Imprint: NIPA | Weight: GMS
This book will be available from 13-Sep-2026
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.
Chapter 1. Foundations of Deep Learning in Medical Imaging
Chapter 2. Edge AI, Quantum Deep Learning and Real-Time Medical Data Processing
Chapter 3. Domain Adaptation and Transfer Learning in Medical Imaging
Chapter 4. Self-Supervised and Semi-Supervised Learning in Healthcare Imaging
Chapter 5. Deep Learning Applications in Oncology Imaging
Chapter 6. Artificial Intelligence in Ophthalmic Imaging and Retinal Disorders
Chapter 7. Deep Learning Approaches in Skin Lesion and Dermoscopy Analysis
Chapter 8. Computational Dermatology and AI-Driven Clinical Applications
Chapter 9. Anomaly Detection and Autoencoder Models in Medical Imaging
Chapter 10. Edge-Based AI Solutions and Clinical Integration in Radiology
Chapter 11. Ethical, Legal and Regulatory Perspectives of AI in Medical Imaging