Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation-笔记
本文结合activate learning和bootstrap。 网络结构如图： 操作过程如下：首先使用少量的标注的数据进行训练网络，然后输入未标记的数据，根据提取的有用信息记录需要标记的数据，然后进行标记。下一个阶段使用现在可利用的标记好的所有数据进行训练，一直循环。挑战就是如何将进行信息提取的FCNs和deep active learning framework（分割网络）结合。 来自FCNs
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相关文章
- 1. 【论文笔记】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 2. 阅读论文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 3. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-笔记
- 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 论文笔记
- 7. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 8. 《Deep Active Learning for Axon-Myelin Segmentation on Histology Data》--阅读笔记-Arxiv
- 9. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 10. 【论文笔记】Deep Residual Learning for Image Recognition