• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on computer science and it applications;KIPS international conference on ubiquitous information technologies >Mid-Level Feature Extractor for Transfer Learning to Small-Scale Dataset of Medical Images
【24h】

Mid-Level Feature Extractor for Transfer Learning to Small-Scale Dataset of Medical Images

机译：中级特征提取器，用于转移到医学图像的小规模数据集

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In fine-tuning-based transfer learning, the size of the dataset may affect the learning accuracy. When a dataset scale is small, fine-tuning-based transfer learning methods use high computing costs, similar to a large-scale dataset. we propose a mid-level feature extractor that only retrains the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with performance of low- and high-level feature extractors, as well as the fine-tuning method. The mid-level feature extractor takes shorter time to converge than other methods, and it shows good accuracy, obtaining an area under the ROC curve (AUC) of 0.87 in untrained test dataset that is very different from training dataset.
机译：在基于微调的转移学习中，数据集的大小可能会影响学习准确性。 当数据集比例小，基于微调的传输学习方法使用高计算成本，类似于大规模数据集。 我们提出了一个中间级别的特征提取器，只能检索中级卷积层，从而提高效率并降低计算成本。 该中级特征提取器可能在培训小规模的医学图像数据集中提供有效的替代方案。 中级特征提取器的性能与低电平和高电平特征提取器的性能进行比较，以及微调方法。 中级特征提取器比其他方法更短的时间来收敛，并且它显示出良好的准确性，在未经训练的测试数据集中获得0.87的ROC曲线（AUC）下的区域，这与训练数据集非常不同。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号