《Dense Residual Network for Retinal Vessel Segmentation》
數據集：IOSTAR，RC-SLO，都要使用Scanning Laser Ophthalmoscopy(SLO)技術採集。 深度密集殘差網絡——DRNet 一、介紹 結合了（U-Net，ResNet，DenseNet）的優點 數據集過少——引入殘差結構學習更過複雜的特徵 跳層結構——解決空間信息丟失 密集連接——最大化的重新利用特徵圖 二、分割算法 2.1、Double Residual Bloc
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相關文章
- 1. Illumination correction by dehazing for retinal vessel segmentation
- 2. Attention Guided Network for Retinal Image Segmentation
- 3. 《Dual Encoding U-Net for Retinal Vessel Segmentation》閱讀筆記-MICCAI2019
- 4. Residual Dense Network for Image Super-Resolution
- 5. MS-NFN Model for Retinal Vessel Segmentation（血管分割）
- 6. Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
- 7. Paper | Dynamic Residual Dense Network for Image Denoising
- 8. Paper | Residual Dense Network for Image Super-Resolution
- 9. Paper notes-Residual Dense Network for Image Super-Resolution
- 10. 《Unsupervised Ensemble Strategy for Retinal Vessel Segmentation》--閱讀筆記-MICCAI2019