DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation解讀
摘要：使用腺體的形態評估腺癌的惡性程度是病理學家常規手段，從解剖結構中準確分割腺體圖像是獲得可靠的形態是統計量化診斷的關鍵一步。在本文中，我們提出了一個高效的深度輪廓感知網絡（DCAN），在統一的多任務學習框架下解決這個具有挑戰性的問題。在提出的網絡中，來自分層結構中的多級上下文特徵被探索來爲準確的腺體分割作爲輔助監督。在訓練過程中加入多任務正則化，可以進一步提高中間特徵的判別能力。而且，我們的網
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相關文章
- 1. zf:【論文閱讀】DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
- 2. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
- 3. 【論文筆記+代碼參考】DCAN: Deep contour-aware networks for object instance segmentation from histology images
- 4. Fully Convolutional Networks for Semantic Segmentation 論文解讀
- 5. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- 6. 論文閱讀——A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
- 7. 醫學圖像分割--Topology Aware Fully Convolutional Networks For Histology Gland Segmentation
- 8. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution閱讀筆記
- 9. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-筆記
- 10. [論文解讀] Concolic Testing for Deep Neural Networks