Deep Poincare Map for Robost Medical Image Segmentation 論文閱讀
Deep Poincare Map for Robost Medical Image Segmentation於17年3月份發表在arXiv上，作者來自帝國理工學院。 這篇文章提出了一種半自動方法（需要初始點和初始向量，但並不是過分依賴），將分割問題看成動態系統，用CNN來預測動態系統的演變，也是屬於深度學習和傳統方法結合的類型。 Motivation 分割問題作爲基本任務，目前解決方案主要有re
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相關文章
- 1. 論文閱讀——LADDERNET: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION
- 2. 【論文閱讀】Deep Residual Learning for Image Recognition
- 3. 【論文閱讀】SegNet A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 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：《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》
- 6. 閱讀論文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 7. 論文閱讀——Deep Residual Learning for Image Recognition
- 8. 文章閱讀：Multi-scale guided attention for medical image segmentation
- 9. 【論文閱讀】2020.CVPR.C2FNAS Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
- 10. [論文翻譯]UNet++: A Nested U-Net Architecture for Medical Image Segmentation