論文筆記：A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
大致框架： 基本流程： 預處理：通過N4ITK以及基於圖像模式的方法實現圖像強度歸一化。 利用全卷積神經網絡（FCNNs）對圖像以像素單位分配不同標籤。 利用條件隨機場所構建的遞歸神經網絡（CRF-RNN）對FCNNs輸出結果進行優化。 利用多數投票策略（Majority voting strategy）對不同標籤標記結果進行分割，實現病變腦組織與正常腦組織的劃分。 後續處理：去噪、去除異常區域、
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相關文章
- 1. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
- 2. Learning Contextual and Attentive Information for Brain Tumor Segmentation
- 3. Brain tumor segmentation with Deep Neural Networks
- 4. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions論文筆記
- 5. LEARNING VOLUMETRIC SEGMENTATION FOR LUNG TUMOR
- 6. 論文閱讀筆記：A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
- 7. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation
- 8. 論文閱讀——A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
- 9. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 10. 【論文筆記】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation