標(biāo)題: Titlebook: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging; Patrick Veit-Haibach,Ken Herrmann Book 2022 The Editor(s) [打印本頁] 作者: 選民 時(shí)間: 2025-3-21 19:12
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging影響因子(影響力)
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging被引頻次
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging被引頻次學(xué)科排名
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging年度引用
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging年度引用學(xué)科排名
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging讀者反饋
書目名稱Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging讀者反饋學(xué)科排名
作者: 火花 時(shí)間: 2025-3-21 22:05
978-3-031-00121-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: PHAG 時(shí)間: 2025-3-22 04:26 作者: 團(tuán)結(jié) 時(shí)間: 2025-3-22 05:17 作者: electrolyte 時(shí)間: 2025-3-22 08:53 作者: 館長 時(shí)間: 2025-3-22 14:48 作者: Formidable 時(shí)間: 2025-3-22 17:06 作者: 廢止 時(shí)間: 2025-3-22 22:17
http://image.papertrans.cn/b/image/162561.jpg作者: Adrenaline 時(shí)間: 2025-3-23 02:05 作者: EVEN 時(shí)間: 2025-3-23 07:11
Introduction to Optimal Control,conditions. However, radiomics parameters are for example influenced by the type of acquisition and reconstruction, the image processing and even the software being used for feature extraction. This chapter will give a short overview over the impact of each step along the radiomic evaluation pipelin作者: 苦惱 時(shí)間: 2025-3-23 12:06
Introduction to Optimal Control,ecognized that AI will completely transform the field. This chapter provides a general overview of some of the advancements in AI techniques, as well as their historical and current uses in medical imaging. It also highlights some areas of emerging research and provides a glimpse of the potential fu作者: 無法治愈 時(shí)間: 2025-3-23 16:35 作者: morale 時(shí)間: 2025-3-23 18:43
Implementing Digital Real-Time Servos, of a normal biological process, a disease, or a response to a therapeutic intervention. Biomarkers have been shown to be useful as a complement to the traditional radiological diagnosis either to detect a specific disorder or lesion; quantify its biological situation; evaluate its progression; stra作者: ESPY 時(shí)間: 2025-3-24 01:22 作者: 針葉 時(shí)間: 2025-3-24 05:04
,Rückkopplung hat alles erschaffen,ological biobanks has not been still performed. Imaging biobanks are organized databases of medical images and associated imaging biomarkers shared among multiple researchers, linked to other biorepositories. Artificial Intelligence, Machine Learning, and more specifically, the use of convolutional 作者: 享樂主義者 時(shí)間: 2025-3-24 10:12
Introduction to Device modeling,ents in the field of medical imaging. Traditional, quantitative image analysis has been indispensable to investigate clinical significance of nuclear medicine molecular imaging in patients with various neurodegenerative diseases. Of the AI techniques, deep learning is consisted of the artificial neu作者: 得體 時(shí)間: 2025-3-24 14:01
Frequency Compensation Techniques,tial of AI in the field of nuclear medicine, and some have reached a level that warrants evaluation in clinical trials. Therefore, this chapter summarizes the application of AI to various nuclear medicine imaging modalities and therapies in the context of oncology, including positron emission tomogr作者: 聲明 時(shí)間: 2025-3-24 18:10
Single Transistor Configurations,essing and reconstruction, demonstrated the feasibility of reducing radiation exposure, and facilitated optimal image segmentation. Clinically, AI was shown to improve the diagnosis of obstructive coronary artery disease and to optimally predict adverse events. In this chapter we present the latest 作者: 調(diào)味品 時(shí)間: 2025-3-24 20:18 作者: aquatic 時(shí)間: 2025-3-25 02:05
Single Transistor Configurations,ccessfully treating multi-genic diseases requires systems-oriented research approach focused on the implication of disease-perturbed molecular interaction networks and pathways. These networks represent crucial relationships among genes and proteins, their mutations, chromosomal aberrations, microRN作者: Between 時(shí)間: 2025-3-25 07:14
Single Transistor Configurations,hine learning in particular, within the field of healthcare. We argue that, going forward, the deliberation and further development of ethics of AI and machine learning should be grounded more strongly in the field of data ethics than it is the case today. This is because of the specific nature of t作者: 遺忘 時(shí)間: 2025-3-25 10:56
Frequency Compensation Techniques,vice organization, improve image quality while reducing patient exposure, and dramatically improve the amount and quality of diagnostic information in our studies. In this chapter, we adopt the point of view of the nuclear medicine physician. We discuss the biggest and most predictable benefits of A作者: languid 時(shí)間: 2025-3-25 13:32 作者: 丑惡 時(shí)間: 2025-3-25 16:30 作者: Dissonance 時(shí)間: 2025-3-25 22:08
Legal and Ethical Aspects of Machine Learning: Who Owns the Data?he digital data that enable machine learning and artificial intelligence. We then turn to the question of ownership, discussing what ownership means, and can mean, in the context of digital data, and who can legitimately own digital data used in and for imaging.作者: 假 時(shí)間: 2025-3-26 01:26
Implementing Digital Real-Time Servos,haracteristics of the development process and validation to finally detail how the process can be applied in hybrid modalities where it is highly relevant to combine the spatial information with the functional one.作者: spondylosis 時(shí)間: 2025-3-26 05:21 作者: notion 時(shí)間: 2025-3-26 09:45 作者: 叢林 時(shí)間: 2025-3-26 15:14 作者: Talkative 時(shí)間: 2025-3-26 18:27
Introduction to Feedback Control,ing and to predict clinical prognosis. Like with other advance statistical methods, the accuracy and generalizability of AI/DL methods is enhanced using large and heterogenous datasets to develop robust AI/DL models and applications that can transform the field of healthcare, hybrid and molecular imaging.作者: Odyssey 時(shí)間: 2025-3-26 23:27 作者: 細(xì)胞膜 時(shí)間: 2025-3-27 04:59
,Rückkopplung hat alles erschaffen,tine requires the accomplishment of regulatory and technical challenges. This chapter addresses the main specifications for the creation of imaging biobanks for molecular imaging as well as the strategies for the integration of AI/ML models to streamline the data extraction from the images at a large scale.作者: Negligible 時(shí)間: 2025-3-27 05:45 作者: Middle-Ear 時(shí)間: 2025-3-27 13:20
Role and Influence of Artificial Intelligence in Healthcare, Hybrid Imaging, and Molecular Imaginging and to predict clinical prognosis. Like with other advance statistical methods, the accuracy and generalizability of AI/DL methods is enhanced using large and heterogenous datasets to develop robust AI/DL models and applications that can transform the field of healthcare, hybrid and molecular imaging.作者: 牽索 時(shí)間: 2025-3-27 17:38 作者: 卡死偷電 時(shí)間: 2025-3-27 21:44 作者: 帶來 時(shí)間: 2025-3-27 23:36
Artificial Intelligence/Machine Learning in Nuclear Medicineing models. In this chapter, we will focus on various methods of deep learning which has been applied to positron emission tomography/computed tomography (PET/CT) imaging of neurodegenerative diseases. This will include the classification of disease, segmentation of region-of-interest, image generation, image processing, and low-dose imaging.作者: BUCK 時(shí)間: 2025-3-28 05:11
Book 2022n the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic..A wide range of clinical applications are discussed, fro作者: landfill 時(shí)間: 2025-3-28 09:00
Single Transistor Configurations,he digital data that enable machine learning and artificial intelligence. We then turn to the question of ownership, discussing what ownership means, and can mean, in the context of digital data, and who can legitimately own digital data used in and for imaging.作者: 使堅(jiān)硬 時(shí)間: 2025-3-28 12:47 作者: 生命層 時(shí)間: 2025-3-28 16:13 作者: atrophy 時(shí)間: 2025-3-28 19:48
Introduction to Optimal Control,principles of machine learning algorithms, starting with basic hardware and data requirements and then introducing methods of increasing complexity, from basic multilayer architectures to autoencoders, U-Nets, and adversarial networks.作者: CHIP 時(shí)間: 2025-3-29 00:17 作者: Reservation 時(shí)間: 2025-3-29 06:48
Single Transistor Configurations, shown to improve the diagnosis of obstructive coronary artery disease and to optimally predict adverse events. In this chapter we present the latest applications of AI in nuclear cardiac imaging, putting an emphasis on the most promising approaches and discussing future directions.作者: Provenance 時(shí)間: 2025-3-29 10:19
Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardizationsoftware being used for feature extraction. This chapter will give a short overview over the impact of each step along the radiomic evaluation pipeline and the challenges which might arise from those concerning feature interpretation.作者: Neonatal 時(shí)間: 2025-3-29 14:57 作者: 圓桶 時(shí)間: 2025-3-29 16:43 作者: 厚顏 時(shí)間: 2025-3-29 23:02 作者: synovium 時(shí)間: 2025-3-30 03:49
the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists978-3-031-00121-5978-3-031-00119-2作者: Tempor 時(shí)間: 2025-3-30 06:21
Correction to: Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardization,作者: peak-flow 時(shí)間: 2025-3-30 11:49 作者: chastise 時(shí)間: 2025-3-30 14:53 作者: Constituent 時(shí)間: 2025-3-30 20:25 作者: 合并 時(shí)間: 2025-3-30 23:28 作者: corpus-callosum 時(shí)間: 2025-3-31 03:05
Book 2022ications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice..As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists作者: alcohol-abuse 時(shí)間: 2025-3-31 05:09 作者: 殺子女者 時(shí)間: 2025-3-31 10:37
Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardizationconditions. However, radiomics parameters are for example influenced by the type of acquisition and reconstruction, the image processing and even the software being used for feature extraction. This chapter will give a short overview over the impact of each step along the radiomic evaluation pipelin作者: 向下 時(shí)間: 2025-3-31 16:12 作者: 腫塊 時(shí)間: 2025-3-31 20:43
The Basic Principles of Machine Learningt of state-of-the-art methods for image reconstruction, enhancement, classification, and analysis..This chapter presents an overview of the operating principles of machine learning algorithms, starting with basic hardware and data requirements and then introducing methods of increasing complexity, f作者: Ordnance 時(shí)間: 2025-3-31 21:52 作者: 并入 時(shí)間: 2025-4-1 02:22
