LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
理解論文 LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR ABSTRACT 1.提出了一種方法解決3D images segmentation。 2.提出的方法是端到端的訓練，並且預測volumetric segmentation outputs。 3.爲了解決前景背景分佈不均衡的問題，使用dice loss和focal loss進行優化。 一、IN
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相關文章
- 1. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 2. lung 分割論文
- 3. Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
- 4. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation 論文解讀與程序復現
- 5. 論文關鍵點分析：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 6. 論文閱讀筆記（四十八）：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 7. [論文翻譯]V-Net:Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 8. Adaptive Feature Recombination and Recalibration for Semantic Segmentation: Application to Brain Tum
- 9. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 10. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation-論文筆記