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Machine Learning in Radiation Oncology

Overview of attention for book
Machine Learning in Radiation Oncology
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 What Is Machine Learning?
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    Chapter 2 Computational Learning Theory
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    Chapter 3 Machine Learning Methodology
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    Chapter 4 Performance Evaluation in Machine Learning
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    Chapter 5 Informatics in Radiation Oncology
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    Chapter 6 Application of Machine Learning for Multicenter Learning
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    Chapter 7 Computerized Detection of Lesions in Diagnostic Images
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    Chapter 8 Classification of Malignant and Benign Tumors
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    Chapter 9 Image-Guided Radiotherapy with Machine Learning
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    Chapter 10 Knowledge-Based Treatment Planning
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    Chapter 11 Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapy
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    Chapter 12 Image-Based Motion Correction
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    Chapter 13 Detection and Prediction of Radiotherapy Errors
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    Chapter 14 Treatment Planning Validation
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    Chapter 15 Treatment Delivery Validation
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    Chapter 16 Bioinformatics of Treatment Response
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    Chapter 17 Modelling of Normal Tissue Complication Probabilities (NTCP): Review of Application of Machine Learning in Predicting NTCP
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    Chapter 18 Modeling of Tumor Control Probability (TCP)
Attention for Chapter 6: Application of Machine Learning for Multicenter Learning
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