標(biāo)題: Titlebook: Machine Learning in Radiation Oncology; Theory and Applicati Issam El Naqa,Ruijiang Li,Martin J. Murphy Book 20151st edition Springer Inter [打印本頁] 作者: 我在爭斗志 時間: 2025-3-21 16:09
書目名稱Machine Learning in Radiation Oncology影響因子(影響力)
書目名稱Machine Learning in Radiation Oncology影響因子(影響力)學(xué)科排名
書目名稱Machine Learning in Radiation Oncology網(wǎng)絡(luò)公開度
書目名稱Machine Learning in Radiation Oncology網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning in Radiation Oncology被引頻次
書目名稱Machine Learning in Radiation Oncology被引頻次學(xué)科排名
書目名稱Machine Learning in Radiation Oncology年度引用
書目名稱Machine Learning in Radiation Oncology年度引用學(xué)科排名
書目名稱Machine Learning in Radiation Oncology讀者反饋
書目名稱Machine Learning in Radiation Oncology讀者反饋學(xué)科排名
作者: Anemia 時間: 2025-3-21 20:31 作者: 多山 時間: 2025-3-22 02:29 作者: BILL 時間: 2025-3-22 05:04 作者: 徹底檢查 時間: 2025-3-22 09:43
iction. 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.978-3-319-35464-4978-3-319-18305-3作者: PANT 時間: 2025-3-22 16:33 作者: 得罪人 時間: 2025-3-22 20:20
Sangkyu Lee,Issam El Naqa, isothermal sections, temperature-composition sections, thermodynamics, materials properties and applications, and miscellanea. Finally, a detailed bibliography of all cited references is provided....In the pr978-3-540-32594-9Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: Morbid 時間: 2025-3-22 22:54 作者: 鐵塔等 時間: 2025-3-23 02:14 作者: Irritate 時間: 2025-3-23 08:56 作者: 關(guān)心 時間: 2025-3-23 11:29 作者: plasma-cells 時間: 2025-3-23 14:32 作者: Angioplasty 時間: 2025-3-23 18:11
Nathalie Japkowicz PhD,Mohak Shah PhDphases, pseudobinary systems, invariant equilibria, liquidus, solidus, and solvus surfaces, isothermal sections, temperature-composition sections, thermodynamics, materials properties and applications, and miscellanea. Finally, a detailed bibliography of all cited references is provided....In the pr作者: MELD 時間: 2025-3-23 23:42 作者: 反感 時間: 2025-3-24 05:57
tional scientists.Also available online in www.springerLink..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: 充滿人 時間: 2025-3-24 07:50
ta of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati作者: dendrites 時間: 2025-3-24 13:21
Issam El Naqa,Martin J. Murphyta of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati作者: Enteropathic 時間: 2025-3-24 17:06 作者: 污點(diǎn) 時間: 2025-3-24 21:35 作者: Harbor 時間: 2025-3-25 01:24
Nathalie Japkowicz PhD,Mohak Shah PhDta of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati作者: 襲擊 時間: 2025-3-25 03:22 作者: Harbor 時間: 2025-3-25 11:04
What Is Machine Learning?vironment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biolog作者: 爭論 時間: 2025-3-25 14:31
Computational Learning Theoryappropriate learning algorithm for a particular task. In this chapter, we present the two main theoretical frameworks—probably approximately correct (PAC) and Vapnik–Chervonenkis (VC) dimension—which allow us to answer questions such as which learning process we should select, what is the learning c作者: Expurgate 時間: 2025-3-25 18:12 作者: Ptsd429 時間: 2025-3-25 23:54 作者: 水槽 時間: 2025-3-26 02:18
Informatics in Radiation Oncologyuestions and an abundance of data, machine learning technologies can be valuable. Available data includes handwritten notes on paper, imaging data available in digital formats, radiation treatment plan details, financial data, and multilevel multicenter databases, to name a few. Tools of various com作者: garrulous 時間: 2025-3-26 05:04
Application of Machine Learning for Multicenter Learning treatment options, as more information is needed to make an informed decision. One of the methods is to use machine-learning techniques to develop predictive models. Although prediction models, embedded in clinical decision support systems (CDSSs), are the foreseen solution, developing/training suc作者: 倫理學(xué) 時間: 2025-3-26 10:13 作者: daredevil 時間: 2025-3-26 14:04 作者: Mechanics 時間: 2025-3-26 19:14 作者: Adulate 時間: 2025-3-27 00:56
Knowledge-Based Treatment Planningm the treating team of a current pending case. This notion of using prior treatment planning information constitutes the underlying principle of the so-called knowledge-based treatment planning (KBTP). In this chapter, we will discuss KBTP and provide some examples highlighting its current status, t作者: 榮幸 時間: 2025-3-27 03:18 作者: Serenity 時間: 2025-3-27 08:50 作者: 節(jié)省 時間: 2025-3-27 12:15 作者: 不如樂死去 時間: 2025-3-27 15:41
Treatment Planning Validationand established techniques for detecting errors in radiotherapy. We will discuss the rationale for using machine learning to detect large errors or outliers in radiotherapy treatment plans. As a concrete example, an automated error detection system for radiation treatment plans will be described. Th作者: 積習(xí)難改 時間: 2025-3-27 18:04
Issam El Naqa,Ruijiang Li,Martin J. MurphyProvides a complete overview of the role of machine learning in radiation oncology and medical physics.Covers the use of machine learning in quality assurance, computer-aided detection, image-guided r作者: 半導(dǎo)體 時間: 2025-3-27 23:38
http://image.papertrans.cn/m/image/620700.jpg作者: 口音在加重 時間: 2025-3-28 05:12 作者: 健忘癥 時間: 2025-3-28 06:36 作者: 安心地散步 時間: 2025-3-28 14:26 作者: 鬼魂 時間: 2025-3-28 16:12
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.作者: VICT 時間: 2025-3-28 20:53 作者: 激怒 時間: 2025-3-29 02:15
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.作者: intention 時間: 2025-3-29 05:11 作者: 碎石頭 時間: 2025-3-29 09:31 作者: progestogen 時間: 2025-3-29 14:58
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.作者: 泥沼 時間: 2025-3-29 17:12
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.作者: Tincture 時間: 2025-3-29 20:37 作者: Merited 時間: 2025-3-30 00:20
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.作者: 烤架 時間: 2025-3-30 07:41
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.作者: ostensible 時間: 2025-3-30 10:12 作者: mortuary 時間: 2025-3-30 13:07 作者: Abrupt 時間: 2025-3-30 18:32
10樓作者: 語源學(xué) 時間: 2025-3-30 21:48
10樓
