Titlebook: Machine Learning in Radiation Oncology; Theory and Applicati Issam El Naqa,Ruijiang Li,Martin J. Murphy Book 20151st edition Springer Inter

鬼魂

Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapycan be trained to model individual breathing patterns. Neural networks have proven quite effective in this capacity. This chapter describes the nature of the motion-compensated treatment problem and the issues in using a neural network to handle it.

VICT

激怒

Informatics in Radiation Oncologyilable in digital formats, radiation treatment plan details, financial data, and multilevel multicenter databases, to name a few. Tools of various complexity for various goals are available. The following chapter aims to portray this domain and present a selection of available tools.

intention

碎石头

progestogen

Computational Learning Theoryapacity of the algorithm selected, and under which conditions is successful learning possible or impossible. Practical methods for selecting proper model complexity are presented using techniques based on information theory and statistical resampling.

泥沼

Image-Guided Radiotherapy with Machine Learning we will present and discuss automatic and semiautomatic methods for CT prostate segmentation in the IGRT workflow. In the last section, we will present our extension of some recently developed machine learning approaches to segment the prostate in MR images.

Tincture

Merited

Treatment Planning Validatione technique was based on unsupervised machine learning, i.e., data clustering, and achieved over 90 % success rates in detecting outliers in over 1,000 treatment plans. Finally, future research directions in the clinical applications of machine learning for treatment planning validation will be briefly discussed.

烤架

Book 20151st editioniotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.