論文閱讀筆記（四十八）：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Abstract. This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated
相關文章
- 1. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 2. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation 論文解讀與程序復現
- 3. 論文關鍵點分析：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 4. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 5. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 6. 論文閱讀筆記（四十五）：Deformable Convolutional Networks
- 7. LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
- 8. 論文閱讀筆記（五十二）：Outline Objects using Deep Reinforcement Learning
- 9. 論文閱讀筆記（二十七）：Focal Loss for Dense Object Detection
- 10. 論文閱讀筆記（四十四）：Deconvolutional Networks
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相關文章
- 1. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 2. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation 論文解讀與程序復現
- 3. 論文關鍵點分析：3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
- 4. 論文閱讀筆記（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 5. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 6. 論文閱讀筆記（四十五）：Deformable Convolutional Networks
- 7. LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
- 8. 論文閱讀筆記（五十二）：Outline Objects using Deep Reinforcement Learning
- 9. 論文閱讀筆記（二十七）：Focal Loss for Dense Object Detection
- 10. 論文閱讀筆記（四十四）：Deconvolutional Networks