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Machine Learning in Radiation Oncology Theory and Applications /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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).