鬼魂
发表于 2025-3-28 16:12:52
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
发表于 2025-3-28 20:53:38
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激怒
发表于 2025-3-29 02:15:30
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
发表于 2025-3-29 05:11:31
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碎石头
发表于 2025-3-29 09:31:52
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progestogen
发表于 2025-3-29 14:58:05
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
发表于 2025-3-29 17:12:05
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
发表于 2025-3-29 20:37:45
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Merited
发表于 2025-3-30 00:20:09
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
发表于 2025-3-30 07:41:21
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.