Department of Opto-electronics, Sichuan University, Chengdu, 610064, China.
Med Biol Eng Comput. 2020 Jul;58(7):1575-1582. doi: 10.1007/s11517-020-02180-2. Epub 2020 May 16.
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.
提出了一种基于孪生网络的计算机辅助人外周血白细胞图像分类方法。首先，设计了一个具有两个相同卷积神经网络（CNN）子网络和一个用于白细胞五类分类的逻辑回归的孪生网络，它不仅可以学习区分特征，还可以学习相似性度量。然后，对于白细胞的每种类别，由血液学家选择一个典型样本。为了训练孪生网络，白细胞和属于同一类别的典型样本组成一个真实对，白细胞与其余四个典型样本分别组成四个伪造对。显然，真实对的数量少于伪造对的数量。因此，使用适用于白细胞的一种数据增强方法来丰富真实对的数量。通过使用真实对和伪造对来训练孪生网络，孪生网络不仅可以缩短白细胞与同一类别典型样本之间的相似性度量，而且可以增加白细胞与不同类别典型样本之间的相似性度量。实验结果表明，所提出的方法可以达到 98.8%的平均测试准确率。
