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

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. ### Competing Interest Statement RM-M, LL, ED, ML-O, DB-P work for Spotlab and/or hold shares or phantom shares of Spotlab. ### Funding Statement This project has been partially funded by the European Union's Horizon 2020 research and innovation programme (grant agreement No 881062) and the Bill and Melinda Gates Foundation (grant number Edge-Spot project INV-051355). LL was supported by a predoctoral grant IND2019/TIC-17167 (Comunidad de Madrid). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained from the Research Ethics Committee (REC) Instituto de Salud Carlos III, Spain (CEI PI 74_2020) and the favorable report of the bioethics committee of the faculty of medicine of the Universidad Mayor de San Simon (Cochabamba, Bolivia). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Images and labels can be shared for research purposes upon request. Please contact Miguel Luengo-Oroz (miguel@spotlab.org).