VNC-Net architecture.
<p>The model takes contrast enhanced dual-energy CT (CE DECT) images as input and generates virtual non-contrast (VNC) images as output. The numbers denoted below the encoder/decoder blocks represent the number of channels. The bottom of the figure depicts the structure of the down-sampling bl...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| الملخص: | <p>The model takes contrast enhanced dual-energy CT (CE DECT) images as input and generates virtual non-contrast (VNC) images as output. The numbers denoted below the encoder/decoder blocks represent the number of channels. The bottom of the figure depicts the structure of the down-sampling block of the encoder, which comprises a UnetConv2D block and a max pooling layer. The UnetConv2D component includes two ConvBNReLU blocks, each consisting of a Conv layer that executes 2D convolutional operations, followed by batch normalization and a ReLU activation function.</p> |
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