Maculate
发表于 2025-3-21 20:09:55
书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0281990<br><br> <br><br>书目名称Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0281990<br><br> <br><br>
轻弹
发表于 2025-3-21 21:57:37
https://doi.org/10.1007/978-1-349-00463-8d on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.
WATER
发表于 2025-3-22 01:08:48
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remission
发表于 2025-3-22 06:02:39
https://doi.org/10.1007/978-1-4684-6724-6ification and segmentation tasks. We observe 50–80% reduction in model size, 60–80% lesser number of parameters, 40–85% fewer FLOPs and 65–80% more energy efficiency during inference on CPUs. The code will be available at ..
斗志
发表于 2025-3-22 09:10:59
A Specificity-Preserving Generative Model for Federated MRI Translationd on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.
aspect
发表于 2025-3-22 16:05:57
Towards Real-World Federated Learning in Medical Image Analysis Using Kaapanaframework used in RACOON to enable real-world federated learning in clinical environments. In addition, we create a benchmark of the nnU-Net when applied in multi-site settings by conducting intra- and cross-site experiments on a multi-site prostate segmentation dataset.
aspect
发表于 2025-3-22 18:43:19
Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Netification and segmentation tasks. We observe 50–80% reduction in model size, 60–80% lesser number of parameters, 40–85% fewer FLOPs and 65–80% more energy efficiency during inference on CPUs. The code will be available at ..
煞费苦心
发表于 2025-3-23 00:55:41
Conference proceedings 2022 Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event..DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusi
冥想后
发表于 2025-3-23 01:25:38
Prototype Thermoballistic Model,obustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model’s size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.
征税
发表于 2025-3-23 06:52:57
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