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首页> 外文期刊>European radiology >Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification
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Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

机译：数字乳房造影与数字乳房X线摄影：图像方式的集成增强了基于深度学习的乳房分类

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摘要

Objective To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breast cancer. Methods We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal: (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared. Results 2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (Delta AUC = 0.059 +/- 0.009, p < 0.001), benign (Delta AUC = 0.095 +/- 0.010, p < 0.001) and normal tissue (Delta AUC = 0.042 +/- 0.004, p < 0.001) (paired samples t test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant: Delta AUC = 0.014 +/- 0.014, p = 0.037; benign: Delta AUC = 0.031 +/- 0.006, p < 0.001; normal: Delta AUC = 0.017 +/- 0.004, p < 0.001) (independent samples t test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM: Delta AUC = 0.010 +/- 0.008, p < 0.001; DBT: Delta AUC = 0.009 +/- 0.005, p < 0.001) and normal (FFDM: Delta AUC = 0.005 +/- 0.003, p < 0.001; DBT: Delta AUC = 0.002 +/- 0.002, p < 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification. Conclusions Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer.
机译：目的探讨利用数字乳房断层合成（DBT）或/和全场数字乳房X线摄影（FFDM）的影响，以及对深度卷积神经网络（DCNN）的不同转移学习策略 - 基于乳腺癌的质量分类。方法回顾性地收集了441名DBT和FFDM的患者，涉及涵盖恶性，良性和正常组织的感兴趣区域（ROI），用于DCNN培训和验证。在区分恶性/良性/正常的任务进行实验：（1）DBT与FFDM的分类能力，转让学习的作用验证了2D-DCNN; （2）探讨了DBT和FFDM结合的不同策略以及对分类的相关影响; （3）比较2D-DCNN和3D-DCNN从划痕和体积DBT接受的3D-DCNN。结果2D-DCNN具有转移学习的表现优于DBT在区分恶性的情况下（Delta Auc = 0.059 +/- 0.009，P <0.001），良性（Delta Auc = 0.095 +/- 0.010，P <0.001）和正常组织（Delta AUC = 0.042 +/- 0.004，P <0.001）（配对样品T测试）。在DBT（带传输学习）上培训的2D-DCNN达到比FFDM上的准确性更高（恶性：Delta Auc = 0.014 +/- 0.014，P = 0.037;良性：Delta Auc = 0.031 +/- 0.006，P <0.001;正常：Delta Auc = 0.017 +/- 0.004，p <0.001）（独立样品T测试）。采用DBT和FFDM进行培训的2D-DCNN在良性（FFDM：Delta Auc = 0.010 +/- 0.008，P <0.001; DBT：Delta Auc = 0.009 +/- 0.005，P <0.001）和正常（ FFDM：Delta AUC = 0.005 +/- 0.003，P <0.001; DBT：Delta Auc = 0.002 +/- 0.002，P <0.001）（相关样品弗里德曼测试）。 3D-DCNN和2D-DCNN从划痕培训，只有DBT才会产生中等分类。结论转移学习为DBT和FFDM的质量分类有利，DBT优于转移学习时FFDM。在DCNN培训中集成DBT和FFDM增强了乳腺癌的质量分类准确性。

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  • 来源
    《European radiology》 |2020年第2期|共11页
  • 作者

    Li Xin; Qin Genggeng; He Qiang; Sun Lei; Zeng Hui; He Zilong; Chen Weiguo; Zhen Xin; Zhou Linghong;

  • 作者单位

    Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China;

    Southern Med Univ Sch Biomed Engn Guangzhou 510515 Guangdong Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    Breast; Mammography; Deep learning; Neural network (computer); Classification;

    机译：乳房;乳房X光学术;深度学习;神经网络（计算机）;分类;

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