How many labels do I need? Self-supervised learning strategies for multiple blood parasites classification in microscopy images

Abstract

Bloodborne parasitic diseases such as malaria, filariasis or chagas pose significant challenges in clinical diagnosis, with microscopy as the primary tool for diagnosis. However, limitations such as time-consuming processes and the dependence on trained microscopists is critical, particularly in resource-constrained settings. Deep learning techniques have shown value to interpret microscopy images using large annotated databases for training. In this work, we propose a methodology leveraging self-supervised learning as a foundational model for blood parasite classification. Using a large unannotated database of blood microscopy images, the model is able to learn important image representations that are subsequently transferred to perform parasite classification of 11 different species of parasites requiring a smaller amount of labeled data. Our results show enhanced performance over fully supervised approaches, with ~100 labels per class sufficient to attain an F1 score of ~0.8. This approach is promising for advancing in-vitro diagnostic systems in primary healthcare settings.