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基于深度主动学习的白带白细胞智能检测方法研究

鞠孟汐 李欣蔚 李章勇

鞠孟汐, 李欣蔚, 李章勇. 基于深度主动学习的白带白细胞智能检测方法研究[J]. 仁和测试, 2020, 37(3): 519-526. doi: 10.7507/1001-5515.201909040
引用本文: 鞠孟汐, 李欣蔚, 李章勇. 基于深度主动学习的白带白细胞智能检测方法研究[J]. 仁和测试, 2020, 37(3): 519-526. doi: 10.7507/1001-5515.201909040
Mengxi JU, Xinwei LI, Zhangyong LI. Detection of white blood cells in microscopic leucorrhea images based on deep active learning[J]. Rhhz Test, 2020, 37(3): 519-526. doi: 10.7507/1001-5515.201909040
Citation: Mengxi JU, Xinwei LI, Zhangyong LI. Detection of white blood cells in microscopic leucorrhea images based on deep active learning[J]. Rhhz Test, 2020, 37(3): 519-526. doi: 10.7507/1001-5515.201909040

基于深度主动学习的白带白细胞智能检测方法研究

doi: 10.7507/1001-5515.201909040
基金项目: 全新动态DR关键技术研发与产品开发(cstc2017zdcy-zdyfX0049);重庆市教委科学技术研究计划项目(KJQN201800622);国家自然科学基金项目(61601072,61801069)
详细信息
    通讯作者:

    李章勇,Email:lizy@cqupt.edu.cn

Detection of white blood cells in microscopic leucorrhea images based on deep active learning

Funds: Chongqing Key Industry Common Key Technology Innovation Project; Science and Technology Research Project of Chongqing Education Commission; National Natural Science Foundation of China
More Information
  • 摘要: 白带显微图像中白细胞的数量可以提示阴道炎症的严重程度。目前对白带中白细胞的检测主要依靠医学专家们的人工镜检,这种人工检查耗时、昂贵且容易出错。近年来,有研究提出基于深度学习技术对白带白细胞实现智能检测,但是这类方法通常需要人工标注大量的样本作为训练集,标注代价高。因此,本研究提出运用深度主动学习算法来实现对白带显微图像中白细胞的智能检测。在主动学习框架下,首先以少量的标注样本作为基础训练集,采用更快的卷积神经网络(Faster R-CNN)训练检测模型,再自动挑选最有价值的样本进行人工标注,从而迭代更新训练集和相应的检测模型,使模型的性能不断提高。实验结果表明,深度主动学习技术能在较少的人工标注样本下获得较高的检测精度,对白细胞检测的平均精度达到了 90.6%,可以满足临床常规检查要求。
  • 图  1  白带显微图像

    Figure  1.  Microscopic image of leucorrhea

    图  2  主动学习算法框架

    Figure  2.  Active learning algorithm framework

    图  3  Faster R-CNN 流程图

    Figure  3.  Faster R-CNN flow chart

    图  4  RPN 网络框架

    Figure  4.  RPN network framework

    图  5  ResNet 结构

    Figure  5.  ResNet structure

    图  6  vott 标记的白细胞

    Figure  6.  Vott-labeled white blood cells

    图  7  已标记样本信息表

    Figure  7.  Labeled sample information table

    图  8  交叉验证 10 次在测试集上的检测 MAP 变化图

    Figure  8.  MAP changes on the test set after 10 cross-validations

    图  9  白带显微图像中白细胞测试结果展示

    Figure  9.  Leucocyte test result display in leucorrhea micrograph

    图  10  线索细胞核和白细胞对比

    Figure  10.  Comparison of clue nuclei and leukocytes

    表  1  不同算法在相同数据集上的测试结果对比

    Table  1.   Comparison of test results of different algorithms on the same data set

    方法 训练集 测试集 迭代次数 人工标记图片数量 人工标记耗时 Precision MAP
    主动学习 270 30 10 57 45 min 91.8% 90.6%
    LeNet-5 270 30 0 270 250 min 71.5% 73.3%
    Faster R-CNN 270 30 0 270 250 min 80.4% 82.3%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-17
  • 修回日期:  2020-01-18
  • 刊出日期:  2020-03-17

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