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

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.