Deep Convolutional Neural Networks for breast cancer screening
Deep Convolutional Neural Networks for breast cancer screening
复制标题
深度卷积神经网络用于乳腺癌筛查
DOI：
10.1016/j.cmpb.2018.01.011
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发表时间：
2018-04-01
影响因子：
6.1
通讯作者：
Alheyane, Omar
中科院分区：
文献类型：
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作者：
Chougrad, Hiba;Zouaki, Hamid;Alheyane, Omar
背景和目的：放射科医生往往有一个很难的时间分类乳房摄影肿块病变，导致不必要的乳房活检，以消除怀疑，这最终增加了过高的费用已经负担沉重的病人和医疗保健system.Methods：在本文中，我们开发了一个计算机辅助诊断（CAD）系统的基础上，深度卷积神经网络（CNN），旨在帮助放射科医生分类乳房摄影肿块病变。深度学习通常需要大型数据集来从头开始训练一定深度的网络。迁移学习是处理相对较小数据集的有效方法，例如医学图像，尽管它可能很棘手，因为我们很容易开始过拟合。结果：在这项工作中，我们探索了迁移学习的重要性，并通过实验确定了训练CNN模型时采用的最佳微调策略。我们成功地微调了一些最近最强大的CNN，并取得了比其他对相同公共数据集进行分类的最先进方法更好的结果。例如，我们在DDSM数据库上实现了97.35%的准确性和0.98 AUC，在INbreast数据库上实现了95.50%的准确性和0.97 AUC，在BCDR数据库上实现了96.67%的准确性和0.96 AUC。此外，在对从完整乳房X线照片中提取的所有感兴趣区域（ROI）进行预处理和归一化之后，我们合并了所有数据集，以构建一个大型图像集，并使用它来微调CNN。CNN模型取得了最好的结果，准确率为98.94%，被用作构建乳腺癌筛查框架的基线。为了评估所提出的CAD系统和它的效率来分类新的图像，我们测试了一个独立的数据库（MIAS），并得到98.23%的准确率和0.99 AUC.结论：所获得的结果表明，所提出的框架是performant，确实可以用来预测肿块病变是良性或恶性的。(C)2018 Elsevier B.V.版权所有。
Background and objective: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health care system.Methods: In this paper we developed a Computer-aided Diagnosis (CAD) system based on deep Convolutional Neural Networks (CNN) that aims to help the radiologist classify mammography mass lesions. Deep learning usually requires large datasets to train networks of a certain depth from scratch. Transfer learning is an effective method to deal with relatively small datasets as in the case of medical images, although it can be tricky as we can easily start overfitting.Results: In this work, we explore the importance of transfer learning and we experimentally determine the best fine-tuning strategy to adopt when training a CNN model. We were able to successfully fine-tune some of the recent, most powerful CNNs and achieved better results compared to other state-of-the-art methods which classified the same public datasets. For instance we achieved 97.35% accuracy and 0.98 AUC on the DDSM database, 95.50% accuracy and 0.97 AUC on the INbreast database and 96.67% accuracy and 0.96 AUC on the BCDR database. Furthermore, after pre-processing and normalizing all the extracted Regions of Interest (ROIs) from the full mammograms, we merged all the datasets to build one large set of images and used it to fine-tune our CNNs. The CNN model which achieved the best results, a 98.94% accuracy, was used as a baseline to build the Breast Cancer Screening Framework. To evaluate the proposed CAD system and its efficiency to classify new images, we tested it on an independent database (MIAS) and got 98.23% accuracy and 0.99 AUC.Conclusion: The results obtained demonstrate that the proposed framework is performant and can indeed be used to predict if the mass lesions are benign or malignant. (C) 2018 Elsevier B.V. All rights reserved.