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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: El Naqa, Issam (Επιμελητής έκδοσης), Li, Ruijiang (Επιμελητής έκδοσης), Murphy, Martin J. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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).