Helmet
发表于 2025-3-21 16:58:59
书目名称Medical Applications with Disentanglements影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0629118<br><br> <br><br>书目名称Medical Applications with Disentanglements读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0629118<br><br> <br><br>
Estimable
发表于 2025-3-21 21:42:58
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博爱家
发表于 2025-3-22 04:17:38
978-3-031-25045-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
inculpate
发表于 2025-3-22 07:05:22
Medical Applications with Disentanglements978-3-031-25046-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
祖先
发表于 2025-3-22 09:39:24
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ELUC
发表于 2025-3-22 16:48:57
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Medicare
发表于 2025-3-22 18:40:26
Disentangled Representation Learning for Privacy-Preserving Case-Based Explanationss, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.
羞辱
发表于 2025-3-23 00:52:01
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders augmentation. We show how this framework provides a natural, data-driven approach to probabilistic, instance-specific spatial augmentation. Further, we observe that U-Nets trained on data augmented using this framework compare favorably with U-Nets trained using standard spatial augmentation methods.
争论
发表于 2025-3-23 02:37:54
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bibliophile
发表于 2025-3-23 07:45:49
0302-9743 held in conjunction with MICCAI 2022, in Singapore, on September22, 2022.. The 8 full papers presented in this book together with one short paper were carefully reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wi