Machine Learning in Radiation Oncology Theory and Applications /

• Corporate Author: SpringerLink (Online service)
• Other Authors: El Naqa, Issam (Editor), Li, Ruijiang (Editor), Murphy, Martin J. (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Subjects: Medicine.; Radiotherapy.; Medical physics.; Radiation.; Medicine & Public Health.; Medical and Radiation Physics.

This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised...

Table of Contents:

• Introduction: What is Machine Learning
• Computational Learning Theory
• Overview of Supervised Learning Methods
• Overview of Unsupervised Learning Methods
• Performance Evaluation
• Variety of Applications in Radiation Oncology
• Machine Learning for Quality Assurance: Quality Assurance as a Learning Problem
• Detection of Radiotherapy Errors Using Unsupervised Learning
• Prediction of Radiotherapy Errors Using Supervised Learning
• Machine Learning for Computer-Aided Detection: Detection of Cancer Lesions from Imaging
• Classification of Malignant and Benign Tumours
• Machine Learning for Treatment Planning and Delivery
• Image-guided Radiotherapy with Machine Learning: IMRT Optimization Using Machine Learning
• Treatment Assessment Tools
• Machine Learning for Motion Management: Prediction of Respiratory Motion
• Motion-Correction Using Learning Methods
• Machine Learning Application in 4D-CT
• Machine Learning Application in Dynamic Delivery
• Machine Learning for Outcomes Modeling: Bioinformatics of Treatment Response
• Modelling of Norma Tissue Complication Probabilities (NTCP)
• Modelling of Tumour Control Probability (TCP).