(Imaging in Medical Diagnosis and Therapy)
This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics.
AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided.
This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.
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분야별 서적
Khan's The Physics of Radiation Therapy, 6e
Perez & Brady's Principles and Practice of Radiation Oncology 7/E
WHO Classification of Tumours of the Digestive System, Fourth Edition
Radiation Toxicity: A Practical Medical Guide (Cancer Treatment and Research)
Handbook of Evidence-Based Radiation Oncology
고창순 핵의학
Clinical Chest Ultrasound: From the ICU to the Broncoscopy Suite
Pocket Radiation Oncology Second Edition
Haematolymphoid Tumours: WHO Classification of Tumours 5th Edition, 2Vols set 
