Machine Learning in Radiation Oncology: Theory and Applications

Author:   Issam El Naqa ,  Ruijiang Li ,  Martin J. Murphy
Publisher:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
ISBN:  

9783319354644


Pages:   336
Publication Date:   12 October 2016
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Machine Learning in Radiation Oncology: Theory and Applications


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Author:   Issam El Naqa ,  Ruijiang Li ,  Martin J. Murphy
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2015
Weight:   5.969kg
ISBN:  

9783319354644


ISBN 10:   3319354647
Pages:   336
Publication Date:   12 October 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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).

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