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Article has an altmetric score of 1

Grant support

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).

Analysis of institutional authors

Lin, LinAuthorLuengo-Oroz, MiguelAuthorBermejo-Pelaez, DavidAuthor

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March 13, 2025
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Proceedings Paper
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HOW MANY LABELS DO I NEED? SELF-SUPERVISED LEARNING STRATEGIES FOR MULTIPLE BLOOD PARASITES CLASSIFICATION IN MICROSCOPY IMAGES

Publicated to:Proceedings (International Symposium On Biomedical Imaging). - 2024-01-01 (), DOI: 10.1101/2024.02.29.24303535

Authors: Mancebo-Martin, Roberto; Lin, Lin; Dacal, Elena; Luengo-Oroz, Miguel; Bermejo-Pelaez, David

Affiliations

CIBER BBN, Madrid, Spain - Author
Spotlab SL, Madrid, Spain - Author
Univ Politecn Madrid, Biomed Image Technol, Madrid, Spain - Author

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 similar to 100 labels per class sufficient to attain an F1 score of similar to 0.8. This approach is promising for advancing in-vitro diagnostic systems in primary healthcare settings.

Keywords

Blood parasiteBlood parasitesDeep learningMicroscopySelf-supervised learning

Quality index

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 3.

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (BERMEJO PELAEZ, DAVID).