【论文笔记】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
一 概述 1.本文研究的问题为： 在有限的标注工作中，哪些样例应当被标注，以此可以实现最好的识别效果。 2.解决方法： 设计了 一种结合了FCN和主动学习的深度主动学习框架 ，通过在最有效的注释区域提出有判断力的建议，来显著减少注释工作量。 文章使用FCN产生的不确定性和相似性信息，组成最大集覆盖问题的广义版本，来决定最具代表性和不确定性的区域来注释。 选择 主动学
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相关文章
- 1. 阅读论文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 2. Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation-笔记
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