Department of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, Andhra Pradesh, 522503, India.
Bionanotechnology and Sustainable Laboratory, Department of Biological Sciences, School of Engineering and Sciences, SRM University-AP, Amaravati, Andhra Pradesh, 522503, India.
Med Biol Eng Comput. 2023 Jun;61(6):1549-1563. doi: 10.1007/s11517-023-02804-3. Epub 2023 Feb 17.
Automated classification of blood cells from microscopic images is an interesting research area owing to advancements of efficient neural network models. The existing deep learning methods rely on large data for network training and generating such large data could be time-consuming. Further, explainability is required via class activation mapping for better understanding of the model predictions. Therefore, we developed a Siamese twin network (STN) model based on contrastive learning that trains on relatively few images for the classification of healthy peripheral blood cells using EfficientNet-B3 as the base model. Hence, in this study, a total of 17,092 publicly accessible cell histology images were analyzed from which 6% were used for STN training, 6% for few-shot validation, and the rest 88% for few-shot testing. The proposed architecture demonstrates percent accuracies of 97.00, 98.78, 94.59, 95.70, 98.86, 97.09, 99.71, and 96.30 during 8-way 5-shot testing for the classification of basophils, eosinophils, immature granulocytes, erythroblasts, lymphocytes, monocytes, platelets, and neutrophils, respectively. Further, we propose a novel class activation mapping scheme that highlights the important regions in the test image for the STN model interpretability. Overall, the proposed framework could be used for a fully automated self-exploratory classification of healthy peripheral blood cells. The whole proposed framework demonstrates the Siamese twin network training and 8-way k-shot testing. The values indicate the amount of dissimilarity.
基于高效神经网络模型的发展，自动从显微镜图像中分类血细胞是一个很有趣的研究领域。现有的深度学习方法依赖于大量的数据进行网络训练，而生成如此大量的数据可能很耗时。此外，需要通过类激活映射来实现可解释性，以便更好地理解模型预测。因此，我们开发了一种基于对比学习的孪生网络（STN）模型，该模型使用 EfficientNet-B3 作为基础模型，对相对较少的图像进行训练，用于健康外周血细胞的分类。因此，在这项研究中，总共分析了 17092 张公开可用的细胞组织学图像，其中 6%用于 STN 训练，6%用于 few-shot 验证，其余 88%用于 few-shot 测试。所提出的架构在 8-way 5-shot 测试中，分别对嗜碱性粒细胞、嗜酸性粒细胞、未成熟粒细胞、成红细胞、淋巴细胞、单核细胞、血小板和中性粒细胞的分类，展示了 97.00%、98.78%、94.59%、95.70%、98.86%、97.09%、99.71%和 96.30%的准确率。此外，我们提出了一种新的类激活映射方案，用于突出 STN 模型可解释性测试图像中的重要区域。总的来说，所提出的框架可用于全自动的健康外周血细胞分类探索。整个所提出的框架展示了孪生网络的训练和 8-way k-shot 测试。值表示差异程度。
