Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.
Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.
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DOI：
10.3390/diagnostics11030528
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发表时间：
2021-03-16
期刊：
影响因子：
--
通讯作者：
Bardou D
中科院分区：
文献类型：
--
作者：
Boumaraf S;Liu X;Wan Y;Zheng Z;Ferkous C;Ma X;Li Z;Bardou D
乳腺癌是对女性的严重威胁。许多基于机器学习的计算机辅助诊断(CAD)方法已被提出用于基于组织病理图像的乳腺癌早期诊断。尽管许多这样的分类方法达到了很高的精度，但其中许多方法缺乏对分类过程的解释。在本文中，我们比较了传统机器学习(CML)和基于深度学习(DL)的方法的性能。我们还为组织病理学图像中的乳腺癌分类任务提供了直观的解释。对于基于CML的方法，我们使用三个特征提取器提取一组手工制作的特征，并将它们融合得到图像表示，作为输入来训练五个经典的分类器。对于基于DL的方法，我们采用了著名的VGG-19深度学习体系结构的转移学习方法，其中它在大规模ImageNet上的预训练版本是在组织病理图像上进行块式微调的。在公开可用的BreaKHis数据集上对所提出的方法进行了评估，并在具有20个图像类的非放大组织病理学数据集Kimia Path 960上进行了进一步的验证。在给出了CML和DL方法的分类结果后，为了更好地解释分类性能的差异，我们将学习到的特征可视化。对于基于DL的方法，我们使用注意图直观地可视化最佳微调深度神经网络的感兴趣区域，以解释决策过程并提高所提出模型的临床可解释性。直观的解释可以内在地提高病理学家对自动化DL方法的信任，使其成为乳腺癌诊断的可信和值得信赖的支持工具。结果表明，二分类正确率在94.05%~98.13%之间，八类正确率在76.77%~88.95%之间；二分类正确率在85.65%~89.32%之间，八类正确率在63.55%~69.69%之间。
Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.
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影响因子：
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作者：
Boumaraf, Said;Liu, Xiabi;Ma, Xiaohong
通讯作者：
Ma, Xiaohong
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
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作者：
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