Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
理解論文 Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet 一、摘要 1.提出了一種數據驅動方法用於肺腫瘤分割通過使用Recurrent 3D-DenseUNet。 2.使用大小爲 256*256*8 的image-volumes with tumors 來訓練網絡。 3.還使用了數據驅動的自適應加權方法來區
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相關文章
- 1. LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
- 2. lung cancer detection
- 3. lung 分割論文
- 4. 3D MRI brain tumor segmentation using autoencoder regularization
- 5. Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
- 6. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
- 7. TianCHi/LUNA16/Kaggle lung cancer肺結節數據集特徵比較
- 8. [MICCAI2019]Integrating Cross-modality Hallucinated MRI with CT to Aid Mediastinal Lung Tumor
- 9. Brain tumor segmentation with Deep Neural Networks
- 10. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks_2017