V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation-論文筆記
代碼開源 代碼開源 方法： 說說網絡結構。卷積核大小5X5。每一層增加殘差學習，論文說增加殘差能夠加速收斂。沒有使用pooling，使用卷積來降採樣（核大小2x2x2，步長2）。非線性函數使用PReLu。是右邊使用反捲積。左右兩邊通過拼接而不是相加。 卷積和反捲積如下： 每層輸出特徵感受眼如下： 損失函數： 本文提出一個基於dice coefficient的損失函數。兩個二進制的矩陣的dice相似
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相關文章
- 1. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 2. [論文翻譯]V-Net:Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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- 4. 論文筆記：Fully Convolutional Networks for Semantic Segmentation
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- 8. AutoML論文筆記（十三）Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segment
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