A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network is proposed. Firstly, a Siamese network with two identical convolutional neural network (CNN) sub-networks and a logistic regression for leukocyte five classification is designed, which can learn not only distinguishing features but also a similarity metric. Then for each category of the leukocytes, a typical sample is selected by the hematologist. To train the Siamese network, a leukocyte and a typical sample that belong to the same category make up a genuine pair and the leukocyte with the rest four typical samples respectively make up four impostor pairs. Obviously, the number of the genuine pairs is lesser than that of the impostor pairs. Thus, a data augmentation method suitable for leukocyte is used to enrich the amount of the genuine pairs. By training the Siamese network using the genuine pairs and impostor pairs, the Siamese network can not only shorten the similarity metric between the leukocyte and the same category of the typical sample but also increase the similarity metrics between the leukocyte and the different categories of the typical samples. Experimental results indicate that the proposed method can achieve 98.8% average testing accuracy. Graphical abstract.

中文翻译：

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基于连体网络的计算机辅助人外周血白细胞图像分类方法。

提出了一种基于连体网络的计算机辅助人外周血白细胞图像分类方法。首先，设计了具有两个相同的卷积神经网络（CNN）子网络和白细胞五分类的逻辑回归的暹罗网络，该网络不仅可以学习区分特征，而且可以学习相似度。然后，针对每种类别的白细胞，血液学家选择一个典型的样本。为了训练连体网络，属于同一类别的白细胞和典型样本组成一个真正的对，而其余四个典型样本的白细胞分别组成四个冒名顶替者。显然，真正对的数量要少于冒名顶替者对的数量。从而，一种适用于白细胞的数据增强方法被用来丰富真正对的数量。通过使用真正的对和冒名顶替者对训练来训练暹罗网络，暹罗网络不仅可以缩短白细胞与典型样本的同一类别之间的相似性度量，而且可以提高白细胞与典型样本的不同类别之间的相似性度量样品。实验结果表明，该方法可以达到98.8％的平均测试精度。图形概要。暹罗网络不仅可以缩短白细胞与典型样本同一类别之间的相似度，而且可以提高白细胞与典型样本不同类别之间的相似度。实验结果表明，该方法可以达到98.8％的平均测试精度。图形概要。暹罗网络不仅可以缩短白细胞与典型样本同一类别之间的相似度，而且可以提高白细胞与典型样本不同类别之间的相似度。实验结果表明，该方法可以达到98.8％的平均测试精度。图形概要。