GOLF
发表于 2025-3-21 16:51:52
书目名称Deep Generative Models影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0264555<br><br> <br><br>书目名称Deep Generative Models读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0264555<br><br> <br><br>
HEED
发表于 2025-3-21 21:37:11
Deep Generative Models978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
Terrace
发表于 2025-3-22 01:38:28
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领先
发表于 2025-3-22 04:51:37
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诱导
发表于 2025-3-22 12:09:37
Abstract Factory (Abstract Factory),s clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative ad
Digitalis
发表于 2025-3-22 13:35:19
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Digitalis
发表于 2025-3-22 18:51:36
Wilian Gatti Jr,Beaumie Kim,Lynde Tanage analysis. The latent spaces of these models often show semantically meaningful directions corresponding to human-interpretable image transformations. However, until now, their exploration for medical images has been limited due to the requirement of supervised data. Several methods for unsupervi
谈判
发表于 2025-3-22 21:15:59
Wilian Gatti Jr,Beaumie Kim,Lynde Tane problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training. By leveraging recent advances in generative diffusion probabilistic models (DPM), we synthesize counterfactuals of “How would a patient appear if . pathology was not present?”. The differen
CRACK
发表于 2025-3-23 02:47:50
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eardrum
发表于 2025-3-23 08:31:21
Requirements and Specificationslete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is tra