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Few-shot learning using explainable Siamese twin network for the automated classification of blood cells

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Abstract

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.

Graphical abstract

The whole proposed framework demonstrates the Siamese twin network training and 8-way k-shot testing. The values indicate the amount of dissimilarity.

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Acknowledgements

We would like to thank Hospital Clinic Barcelona for providing the dataset and SRM University—AP for providing the research infrastructure.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University—AP, Amaravati, Andhra Pradesh, 522503, India

    Sudhakar Tummala

  2. Bionanotechnology and Sustainable Laboratory, Department of Biological Sciences, School of Engineering and Sciences, SRM University—AP, Amaravati, Andhra Pradesh, 522503, India

    Anil K. Suresh

Authors
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Correspondence to Sudhakar Tummala.

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This research study was conducted retrospectively using human subject data made available in open access by Hospital Clinic Barcelona. Ethical approval was not required as confirmed by the license attached with the open access data.

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Tummala, S., Suresh, A.K. Few-shot learning using explainable Siamese twin network for the automated classification of blood cells. Med Biol Eng Comput 61, 1549–1563 (2023). https://doi.org/10.1007/s11517-023-02804-3

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