Subtype classification of malignant lymphoma using immunohistochemical staining pattern.
Subtype classification of malignant lymphoma using immunohistochemical staining pattern.
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DOI：
10.1007/s11548-021-02549-0
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发表时间：
2022-07
影响因子：
3
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中科院分区：
文献类型：
--
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对于图像分类问题，构造合适的训练数据对于提高分类器的泛化能力是重要的，特别是当训练数据的大小很小时。我们提出了一种方法，定量评估的典型性的苏木精和伊红（H&E）染色的组织载玻片从一组免疫组织化学（IHC）染色和应用的典型性的实例选择的分类器，预测恶性淋巴瘤的亚型，以提高泛化能力的建设。我们通过低维嵌入空间上的IHC染色模式的概率密度的比率来定义H& E染色的组织切片的典型性。采用基于多实例学习的卷积神经网络构建子类型分类器，不需要在整个切片图像中指示癌区域的注释，我们通过参考评估的典型性来选择训练数据，以提高泛化能力。我们证明了有效性的实例选择的基础上提出的典型性在三个类的亚型分类的262例恶性淋巴瘤。在实验中，我们证实了典型实例的子类型可以比非典型实例的子类型更准确地预测。此外，证实了基于所提出的典型性的训练数据的实例选择提高了分类器的泛化能力，其中当训练数据被构造为集中于典型实例时，与基线方法相比，分类准确率从0.664提高到0.683。实验结果表明，由IHC染色模式计算的H& E染色组织切片的典型性可作为实例选择的标准，以提高泛化能力，并且在一定的实际限制下，该典型性可用于实例选择。
For the image classification problem, the construction of appropriate training data is important for improving the generalization ability of the classifier in particular when the size of the training data is small. We propose a method that quantitatively evaluates the typicality of a hematoxylin-and-eosin (H&E)-stained tissue slide from a set of immunohistochemical (IHC) stains and applies the typicality to instance selection for the construction of classifiers that predict the subtype of malignant lymphoma to improve the generalization ability. We define the typicality of the H&E-stained tissue slides by the ratio of the probability density of the IHC staining patterns on low-dimensional embedded space. Employing a multiple-instance-learning-based convolutional neural network for the construction of the subtype classifier without the annotations indicating cancerous regions in whole slide images, we select the training data by referring to the evaluated typicality to improve the generalization ability. We demonstrate the effectiveness of the instance selection based on the proposed typicality in a three-class subtype classification of 262 malignant lymphoma cases. In the experiment, we confirmed that the subtypes of typical instances could be predicted more accurately than those of atypical instances. Furthermore, it was confirmed that instance selection for the training data based on the proposed typicality improved the generalization ability of the classifier, wherein the classification accuracy was improved from 0.664 to 0.683 compared with the baseline method when the training data was constructed focusing on typical instances. The experimental results showed that the typicality of the H&E-stained tissue slides computed from IHC staining patterns is useful as a criterion for instance selection to enhance the generalization ability, and this typicality could be employed for instance selection under some practical limitations.
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