標(biāo)題: Titlebook: Machine and Deep Learning in Oncology, Medical Physics and Radiology; Issam El Naqa,Martin J. Murphy Book 2022Latest edition Springer Natu [打印本頁(yè)] 作者: Defect 時(shí)間: 2025-3-21 19:01
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology影響因子(影響力)
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology被引頻次
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology被引頻次學(xué)科排名
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology年度引用
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology年度引用學(xué)科排名
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology讀者反饋
書(shū)目名稱(chēng)Machine and Deep Learning in Oncology, Medical Physics and Radiology讀者反饋學(xué)科排名
作者: ADJ 時(shí)間: 2025-3-21 23:53 作者: LITHE 時(shí)間: 2025-3-22 04:18 作者: 進(jìn)步 時(shí)間: 2025-3-22 06:42 作者: ALB 時(shí)間: 2025-3-22 09:21 作者: NICE 時(shí)間: 2025-3-22 13:36 作者: Plaque 時(shí)間: 2025-3-22 17:24
Conventional Machine Learning Methodse principal component analysis and clustering (unsupervised), logistic regression, neural network, support vector machine, decision tree, Bayesian networks, and naive Bayes (supervised) in addition to reinforcement learning.作者: 農(nóng)學(xué) 時(shí)間: 2025-3-23 00:37
Performance Evaluationof that test. The purpose of this chapter is to review these techniques in so far as they apply to advances in Oncology, Medical Physics, and Radiology and to discuss additional evaluation techniques particularly suited for these tasks.作者: cavity 時(shí)間: 2025-3-23 01:31 作者: calorie 時(shí)間: 2025-3-23 09:25 作者: 掃興 時(shí)間: 2025-3-23 09:55 作者: NATAL 時(shí)間: 2025-3-23 16:50 作者: flourish 時(shí)間: 2025-3-23 18:01
What Are Machine and Deep Learning?s a subcategory of machine learning that allows computers to learn directly from the raw data without the need for human-engineered features. These algorithms are becoming the workhorse in the new era of .. Techniques based on machine and deep learning have been applied successfully in diverse field作者: Magisterial 時(shí)間: 2025-3-24 01:38
Computational Learning Theoryappropriate learning algorithm for a particular task. In this chapter, we present the main theoretical frameworks for machine learning algorithms: probably approximately correct (PAC) and Vapnik–Chervonenkis (VC) dimension. In addition, we discuss the new underlying principles of deep learning. Thes作者: DRILL 時(shí)間: 2025-3-24 03:41
Conventional Machine Learning Methodshese various learning objectives can commonly be formulated in theoretical nomenclatures. This chapter introduces different conventional machine learning algorithms that could cater to readers’ specific learning goals. We intend to provide conceptual outlines of some of the widely used algorithms wi作者: 釘牢 時(shí)間: 2025-3-24 06:42 作者: endoscopy 時(shí)間: 2025-3-24 12:22
Quantum Computing for Machine Learning to overcome current challenges in classical machine learning. It utilizes principles of quantum mechanics long known in physics such as superposition and entanglement to perform more efficient and potentially more advanced computational tasks beyond what current classical algorithms can offer. In t作者: brassy 時(shí)間: 2025-3-24 16:00 作者: 彎腰 時(shí)間: 2025-3-24 22:14 作者: Inoperable 時(shí)間: 2025-3-24 23:51 作者: 忘川河 時(shí)間: 2025-3-25 04:16
Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Modelser of medical images are produced which physicians/radiologists must read. They may overlook lesions from such a large number of medical images. Consequently, CADe that provides suspicious lesions with radiologists/physicians is developed and becoming indispensable in their decision-making to preven作者: 無(wú)效 時(shí)間: 2025-3-25 11:16 作者: Concerto 時(shí)間: 2025-3-25 12:54
Auto-contouring for Image-Guidance and Treatment Planningentation of targets and normal tissues has been growing in clinical use as it can mitigate the inter- and intra-observer differences of manual segmentation and significantly reduce contouring time. Auto-segmentation has gone through advances over the years as computer technology has improved. The fi作者: stress-test 時(shí)間: 2025-3-25 17:02
Machine Learning Applications in Quality Assurance of Radiation Deliveryremains within the realm of research applications, a direct connection with clinical workflows is established whenever possible. The chapter begins with a general discussion of the application of ML to QA, before diving into the analysis of Automatic Chart Review, Linac QA, and Virtual Intensity-Mod作者: GRIEF 時(shí)間: 2025-3-25 21:43
Knowledge-Based Treatment Planningand critically important technology for cancer treatment. IMRT treatments rely heavily on planning expertise due to its technical complexity and the conflicting nature of maximizing tumor control while minimizing normal organ damage. As treatment planning experience and especially the carefully desi作者: 肉體 時(shí)間: 2025-3-26 02:07 作者: 漫不經(jīng)心 時(shí)間: 2025-3-26 04:31 作者: 硬化 時(shí)間: 2025-3-26 09:39
What Are Machine and Deep Learning?cians in their pursuit to realize precision medicine. This includes but is not limited to applications in computer-aided detection, classification, and diagnosis in radiology and auto-contouring, treatment planning, response modeling (radiomics, radiogenomics), image-guidance, motion tracking, and q作者: 食品室 時(shí)間: 2025-3-26 13:22 作者: 拖債 時(shí)間: 2025-3-26 19:44
Auto-contouring for Image-Guidance and Treatment Plannings. There are many different deep learning techniques, with convolutional neural networks being the most commonly used technique for segmentation tasks. Before implementation in clinics, careful QA must be carried out for auto-segmentation tasks, such as comparison with clinically approved manual con作者: 過(guò)多 時(shí)間: 2025-3-26 21:15
Knowledge-Based Treatment Planning. Then we will present several KBP models, including a DVH prediction model, a whole breast radiation therapy (WBRT) fluence prediction model, and a beam angle bouquet model. These are a few samples from the vast amount of KBP literature published in the last years and represent some of the most rec作者: Serenity 時(shí)間: 2025-3-27 02:22
Prediction of Oncology Treatment Outcomesment response. Therefore, recent approaches have utilized increasingly data-driven models incorporating advanced bioinformatics and machine learning tools in which treatment metrics are mixed with other patient- or disease-based prognostic factors in order to improve outcomes prediction. Accurate pr作者: 短程旅游 時(shí)間: 2025-3-27 09:18 作者: BOGUS 時(shí)間: 2025-3-27 10:49 作者: mortuary 時(shí)間: 2025-3-27 16:45 作者: CREST 時(shí)間: 2025-3-27 18:33 作者: overshadow 時(shí)間: 2025-3-27 22:58
Machine and Deep Learning in Oncology, Medical Physics and Radiology作者: 鉗子 時(shí)間: 2025-3-28 05:39
Machine and Deep Learning in Oncology, Medical Physics and Radiology978-3-030-83047-2作者: 密碼 時(shí)間: 2025-3-28 09:40
Book 2022Latest editionware code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members ofapplied machine learning communities..?.作者: acrimony 時(shí)間: 2025-3-28 13:28
Issam El Naqa,Martin J. MurphyReference text for machine and deep learning in oncology, medical physics, and radiology.From theory to practice with examples.Provides a complete overview of the role of machine learning in radiation作者: prediabetes 時(shí)間: 2025-3-28 17:34 作者: 我說(shuō)不重要 時(shí)間: 2025-3-28 18:51
https://doi.org/10.1007/978-3-030-83047-2Machine Learning; Deep Learning; Artificial Intelligence; Medical Physics; Image Analysis; Decision Suppo作者: 骨 時(shí)間: 2025-3-29 01:40 作者: 希望 時(shí)間: 2025-3-29 05:57
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