与生
发表于 2025-3-21 17:10:08
书目名称Deep Learning in Healthcare影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0264620<br><br> <br><br>书目名称Deep Learning in Healthcare读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0264620<br><br> <br><br>
音乐学者
发表于 2025-3-21 22:37:25
Medical Image Segmentation Using Deep Learningllenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-bas
MIRTH
发表于 2025-3-22 03:42:50
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宣传
发表于 2025-3-22 07:06:38
Medical Image Enhancement Using Deep Learning methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network intr
Triglyceride
发表于 2025-3-22 09:53:53
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沟通
发表于 2025-3-22 13:45:54
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沟通
发表于 2025-3-22 18:19:54
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富饶
发表于 2025-3-22 22:30:14
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ineluctable
发表于 2025-3-23 05:12:49
Multi-scale Deep Convolutional Neural Networks for Emphysema Classification and Quantification extracting low-level features or mid-level features without enough high-level information. Moreover, these approaches do not take the characteristics (scales) of different emphysema into account, which are crucial for feature extraction. In contrast to previous works, we propose a novel deep learni
Wallow
发表于 2025-3-23 08:33:04
Opacity Labeling of Diffuse Lung Diseases in CT Images Using Unsupervised and Semi-supervised Learniy deep learning, requires a large number of training data with annotations. Deep learning often requires thousands of training data, but it is tough work for radiologists to give normal and abnormal labels to many images. In this research, aiming the efficient opacity annotation of diffuse lung dise