Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation-筆記
本文結合activate learning和bootstrap。 網絡結構如圖： 操作過程如下：首先使用少量的標註的數據進行訓練網絡，然後輸入未標記的數據，根據提取的有用信息記錄需要標記的數據，然後進行標記。下一個階段使用現在可利用的標記好的所有數據進行訓練，一直循環。挑戰就是如何將進行信息提取的FCNs和deep active learning framework（分割網絡）結合。 來自FCNs
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相關文章
- 1. 【論文筆記】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 2. 閱讀論文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 3. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-筆記
- 4. [DIS]Deep Dual Learning for Semantic Image Segmentation
- 5. Context-Aware U-Net for Biomedical Image Segmentation
- 6. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 論文筆記
- 7. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 8. 《Deep Active Learning for Axon-Myelin Segmentation on Histology Data》--閱讀筆記-Arxiv
- 9. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 10. 【論文筆記】Deep Residual Learning for Image Recognition