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面向乳腺超声分类的低尺度形态特征重校准方法

龚勋 朱丹 杨子奇 罗俊

龚勋, 朱丹, 杨子奇, 罗俊. 面向乳腺超声分类的低尺度形态特征重校准方法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20211061
引用本文: 龚勋, 朱丹, 杨子奇, 罗俊. 面向乳腺超声分类的低尺度形态特征重校准方法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20211061
GONG Xun, ZHU Dan, YANG Ziqi, LUO Jun. Low-Scale Morphological Feature Recalibration Method for Breast Ultrasound Classification[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20211061
Citation: GONG Xun, ZHU Dan, YANG Ziqi, LUO Jun. Low-Scale Morphological Feature Recalibration Method for Breast Ultrasound Classification[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20211061

面向乳腺超声分类的低尺度形态特征重校准方法

doi: 10.3969/j.issn.0258-2724.20211061
基金项目: 国家自然科学基金(61876158);中央高校基本科研业务费科技创新项目(2682021ZTPY030)
详细信息
    作者简介:

    龚勋,男,教授,博士生导师,研究方向为计算机视觉、人工智能、医学图像识别等,E-mail:xgong@swjtu.edu.cn

  • 中图分类号: V221.3

Low-Scale Morphological Feature Recalibration Method for Breast Ultrasound Classification

  • 摘要:

    针对乳腺超声图像具有类内差异大、类间差异小以及结节形状复杂多变等问题,本文提出一种形状特征重校准的乳腺超声图像算法,实现乳腺超声的自动化诊断. 首先,构建端到端的网络模型,采用渐进训练方式,充分学习图像中更具辨别力的区域,获取更细粒度的特征信息;其次,提出分区打乱机制,降低网络中打乱图像时破坏结节区域所产生的噪声;然后,将模型底层提取的特征与通过掩膜图像获得的形状特征进行重校准,提出低尺度重校准损失函数;最后,构建一个包含1550张乳腺超声图像数据集LSRD (low-scale recalibration database),验证所提方法的有效性. 实验结果表明:本文模型在LSRD上准确率94.3%、敏感性91.2%、特异性93.6%、ROC (receiver operator characteristic curve)与坐标围成的面积(area under curve,AUC)为0.941,均优于对比模型;在BUSI (breast ultrasound image)数据集上,相较于对比模型,其分类精度提升3.3%.

     

  • 图 1  分区打乱和重校准网络结构

    Figure 1.  Partition shuffle and recalibration network structure

    图 2  渐进训练算法

    Figure 2.  Progressive training algorithm

    图 3  乳腺超声图像和对应mask图像

    Figure 3.  Breast ultrasound image and corresponding mask image

    图 4  分区打乱机制过程

    Figure 4.  Process of partition shuffle mechanism

    图 5  高、低层特征可视化效果

    Figure 5.  Visualization of high- and low-layer features

    图 6  不同层次形状特征重校准对比

    Figure 6.  Comparison of morphological feature recalibration at different layers

    图 7  本文方法与其它方法对比

    Figure 7.  Comparison between proposed method and other methods

    表  1  不同主干网络分类精度对比

    Table  1.   Comparison of classification accuracy for different backbone networks %

    网络模型 训练集 测试集
    精度 F1 分数 精度 F1 分数
    ResNet18 83.5 82.0 82.3 81.7
    ResNet34 85.6 84.9 84.8 82.3
    ResNet50 90.8 87.6 81.2 76.3
    VGG-16 80.9 79.8 80.6 79.6
    VGG-19 82.5 81.6 80.0 79.5
    下载: 导出CSV

    表  2  分块尺度对比实验

    Table  2.   Comparison experiments of partition scales %

    块尺度
    大小/块
    训练集 训练集
    精度 F1 分数 精度 F1 分数
    16 83.1 82.9 82.8 82.3
    8 84.2 83.9 83.2 82.5
    4 89.6 89.3 88.3 87.7
    2 86.1 85.7 85.3 85.1
    下载: 导出CSV

    表  3  图像打乱对比实验

    Table  3.   Comparison experiments of shuffled images %

    打乱方法 训练集 测试集
    精度 F1 分数 精度 F1 分数
    未打乱 85.6 84.9 84.8 82.3
    随机打乱 88.2 87.9 87.2 86.5
    分区打乱 89.6 89.3 88.3 87.7
    下载: 导出CSV

    表  4  重校准损失函数对分类精度的影响

    Table  4.   Influence of recalibration loss function on classification accuracy %

    损失函数 训练集 测试集
    精度 F1 分数 精度 F1 分数
    Log-Cosh 91.7 91.6 90.3 90.8
    MAE 88.3 88.5 86.1 86.7
    SMAE 89.3 89.1 90.6 90.2
    MSE 93.8 92.5 91.3 91.7
    下载: 导出CSV

    表  5  重校准损失函数和分类损失函数的权重对分类精度的影响

    Table  5.   Influence of weights of recalibration loss function and classification loss function on classification accuracy %

    损失函数权重 训练集 测试集
    Ls Lcla 精度 F1 分数 精度 F1 分数
    1.00 1.00 93.8 92.5 91.3 91.7
    0.25 0.75 90.3 89.7 89.3 88.3
    0.50 0.50 91.5 90.8 90.6 90.2
    0.75 0.25 92.3 91.2 90.8 90.5
    下载: 导出CSV

    表  6  不同融合方法对分类精度的影响

    Table  6.   Influence of different fusion methods on classification accuracy %

    融合方法 训练集 测试集
    融合方法 精度 F1 分数 精度 F1 分数
    Max 93.4 92.9 92.7 92.2
    Sum 92.6 92.0 92.3 91.7
    Conv 94.9 94.5 94.3 93.6
    下载: 导出CSV

    表  7  不同打乱方法和重校准损失函数下的消融实验

    Table  7.   Ablation experiments with different shuffle methods and recalibration loss functions %

    打乱方法 损失函数 训练集 测试集
    精度 F1 分数 精度 F1 分数
    未打乱 Log-Cosh 86.2 85.3 84.8 83.3
    MAE 85.2 84.9 83.7 83.1
    SMAE 85.6 84.7 84.2 83.9
    MSE 86.8 86.2 85.9 85.7
    随机打乱 Log-Cosh 87.2 86.9 87.2 86.3
    MAE 86.2 86.9 86.2 86.3
    SMAE 86.7 86.3 85.8 85.6
    MSE 87.9 87.3 87.1 86.5
    分区打乱 Log-Cosh 91.7 91.6 90.3 90.8
    MAE 88.3 88.5 86.1 86.7
    SMAE 89.3 89.1 90.6 90.2
    MSE 93.8 92.5 91.3 91.7
    下载: 导出CSV

    表  8  BUSI数据集实验结果

    Table  8.   Experimental results of BUSI dataset %

    融合方法 训练集 测试集
    融合方法 精度 F1 分数 精度 F1 分数
    Max 93.4 92.9 92.7 92.2
    Sum 92.6 92.0 92.3 91.7
    Conv 94.9 94.5 94.3 93.6
    下载: 导出CSV

    表  9  本文模型与主流方法性能比较

    Table  9.   Performance comparison between proposed method and popular methods

    方法 数据集(良性、恶性)/幅 准确
    率/%
    敏感
    性/%
    特异
    性/%
    AUC
    文献[21] 184、264 91.1 94.3 86.4
    文献[22] 112、72 82.0 79.4 84.7
    文献[23] 400、400 84.5
    文献[12] LSRD 89.3 83.9 92.1 0.925
    文献[13] LSRD 92.3 90.2 92.4 0.937
    本文模型 LSRD 94.3 91.2 93.6 0.941
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-30
  • 修回日期:  2022-05-20
  • 网络出版日期:  2024-05-08

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