標(biāo)題: Titlebook: Artificial Intelligence in Medical Imaging; Opportunities, Appli Erik R. Ranschaert,Sergey Morozov,Paul R. Algra Book 2019 Springer Nature [打印本頁] 作者: onychomycosis 時(shí)間: 2025-3-21 17:18
書目名稱Artificial Intelligence in Medical Imaging影響因子(影響力)
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書目名稱Artificial Intelligence in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence in Medical Imaging被引頻次
書目名稱Artificial Intelligence in Medical Imaging被引頻次學(xué)科排名
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書目名稱Artificial Intelligence in Medical Imaging年度引用學(xué)科排名
書目名稱Artificial Intelligence in Medical Imaging讀者反饋
書目名稱Artificial Intelligence in Medical Imaging讀者反饋學(xué)科排名
作者: 連鎖 時(shí)間: 2025-3-21 22:48 作者: THROB 時(shí)間: 2025-3-22 01:08
The Value of Structured Reporting for AIadable. Also, through providing clearly defined structures, report templates would facilitate data from other systems to be integrated into the radiological report..This chapter aims to provide an overview of the current state of structured reporting with a special focus on its potential implication作者: 連鎖 時(shí)間: 2025-3-22 08:04
Imaging Biomarkers and Imaging Biobankscuracy in the usage of imaging biomarkers, making it feasible to integrate them in automated pipelines for the generation of massive amounts of radiomic data to be used for storage in imaging biobanks.作者: Cerebrovascular 時(shí)間: 2025-3-22 10:14 作者: obviate 時(shí)間: 2025-3-22 16:45
Book 2019practices. The concluding section focuses on the impact of AI on radiology and the implicationsfor radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imagi作者: chalice 時(shí)間: 2025-3-22 21:01 作者: insidious 時(shí)間: 2025-3-23 01:03 作者: 上流社會(huì) 時(shí)間: 2025-3-23 05:02 作者: 憤怒事實(shí) 時(shí)間: 2025-3-23 06:47
From meat to food: the proteomics assessmentcuracy in the usage of imaging biomarkers, making it feasible to integrate them in automated pipelines for the generation of massive amounts of radiomic data to be used for storage in imaging biobanks.作者: Externalize 時(shí)間: 2025-3-23 12:17
Farm-Level Microsimulation Modellingom imaging is combined with other data such as the results from laboratory evaluations, genetic analysis, medication use and personal fitness trackers. Nevertheless, the process of bringing the results to physicians is nontrivial, and we also discuss our experience with deployment of developed algor作者: 使熄滅 時(shí)間: 2025-3-23 16:27
tionsfor radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imagi978-3-319-94878-2作者: duplicate 時(shí)間: 2025-3-23 18:52
Introduction: Game Changers in Radiology are creating a real hype around artificial intelligence for automated image analysis, hereby exerting external pressure on radiologists to reevaluate the value and future of their profession. Radiologists from their side seem to be rather reluctant to embrace and implement these new technological o作者: Preamble 時(shí)間: 2025-3-23 22:46 作者: Moderate 時(shí)間: 2025-3-24 02:47
A Deeper Understanding of Deep Learningcuss the power of contextual processing, study insights from the human visual system, and study in some detail how the different of a deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.作者: Accessible 時(shí)間: 2025-3-24 07:13
Deep Learning and Machine Learning in Imaging: Basic Principlesly on a class of algorithms known as deep learning. Prior machine learning methods are still useful and can provide a good understanding of machine learning fundamentals. Deep learning methods are still seeing rapid advances, but there are several basic components that are likely to be durable. This作者: chlorosis 時(shí)間: 2025-3-24 13:15 作者: nautical 時(shí)間: 2025-3-24 16:41 作者: ornithology 時(shí)間: 2025-3-24 19:01 作者: 內(nèi)行 時(shí)間: 2025-3-25 01:32 作者: 詞根詞綴法 時(shí)間: 2025-3-25 04:09
Enterprise Imaginged multimedia archives, it is now known as enterprise imaging platform. Several aspects, like governance, interfaces, access and privacy rules, etc., are relevant for a successful implementation of an enterprise imaging platform. Such data repositories could be perfect sources for the development, a作者: 碎石 時(shí)間: 2025-3-25 09:00
Imaging Biomarkers and Imaging Biobanks reproducible, but they also have to show a clear efficacy in the detection and diagnosis of the disease and/or in the evaluation of treatment response. This efficacy must be confirmed by a close relationship with disease hallmarks, which allows them to act as surrogate indicators of relevant clinic作者: 溫室 時(shí)間: 2025-3-25 12:11 作者: 兇殘 時(shí)間: 2025-3-25 17:11 作者: 攤位 時(shí)間: 2025-3-25 22:59
Cardiovascular Diseasesardiovascular disease. Machine learning and deep learning in particular will have a fundamental and lasting impact on all of these modalities. Whereas deep learning is mostly discussed in the context of image interpretation, we show that the impact is much broader than this. The entire imaging chain作者: MOAT 時(shí)間: 2025-3-26 03:58 作者: Colonoscopy 時(shí)間: 2025-3-26 04:22 作者: Dislocation 時(shí)間: 2025-3-26 12:13 作者: Tractable 時(shí)間: 2025-3-26 15:27 作者: 保存 時(shí)間: 2025-3-26 19:03 作者: 拖網(wǎng) 時(shí)間: 2025-3-26 23:19 作者: 圓錐體 時(shí)間: 2025-3-27 04:44 作者: Ankylo- 時(shí)間: 2025-3-27 08:18 作者: ILEUM 時(shí)間: 2025-3-27 13:25 作者: aqueduct 時(shí)間: 2025-3-27 14:24 作者: 你正派 時(shí)間: 2025-3-27 19:36 作者: atopic-rhinitis 時(shí)間: 2025-3-27 22:44
Applications of AI Beyond Image Interpretationg imaging appropriateness and utilization, patient scheduling, exam protocoling, image quality, scanner efficiency, radiation exposure, radiologist workflow and reporting, patient follow-up and safety, billing, research and education, and more to improve, ultimately, patient care.作者: 匍匐前進(jìn) 時(shí)間: 2025-3-28 03:36 作者: 懶鬼才會(huì)衰弱 時(shí)間: 2025-3-28 07:33 作者: Predigest 時(shí)間: 2025-3-28 13:50 作者: 殺菌劑 時(shí)間: 2025-3-28 15:31 作者: 遠(yuǎn)地點(diǎn) 時(shí)間: 2025-3-28 19:36 作者: Libido 時(shí)間: 2025-3-29 02:46 作者: Erythropoietin 時(shí)間: 2025-3-29 05:13 作者: 使迷惑 時(shí)間: 2025-3-29 11:11
https://doi.org/10.1057/9780230390089cuss the power of contextual processing, study insights from the human visual system, and study in some detail how the different of a deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.作者: JUST 時(shí)間: 2025-3-29 15:21
https://doi.org/10.1007/978-3-319-94878-2Artificial Intelligence in Medical Imaging; Deep Learning in Medical Imaging; Machine Learning in Medi作者: 臭名昭著 時(shí)間: 2025-3-29 17:53 作者: GRACE 時(shí)間: 2025-3-29 20:17 作者: fiction 時(shí)間: 2025-3-30 02:43 作者: 苦笑 時(shí)間: 2025-3-30 04:50
https://doi.org/10.1007/978-1-4684-6486-3imaging data in order to support diagnosis, therapy planning and follow-up, and biomedical research. Medical image analysis is complicated by the complexity of the data itself—involving 3D tomographic images acquired with different modalities that are based on different physical principles, each wit作者: 駁船 時(shí)間: 2025-3-30 09:08 作者: 閑聊 時(shí)間: 2025-3-30 13:31
International Political Economy Seriesly on a class of algorithms known as deep learning. Prior machine learning methods are still useful and can provide a good understanding of machine learning fundamentals. Deep learning methods are still seeing rapid advances, but there are several basic components that are likely to be durable. This作者: visual-cortex 時(shí)間: 2025-3-30 20:37
https://doi.org/10.1007/978-981-15-7352-1 clinical episode. AI developments have demonstrated to be highly specific, being useful to solve repetitive and rule-driven problems without clinical context with human-like performance, and must be understood more as a complement than a substitute of the radiologist. The quantity and heterogeneity
