Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

Louise Guillon, Soheib Biga, Axel Puyo, Grégoire Pasquier, Valentin Foucher +7 more
8/16/2024
eess.IVcs.CVq-bio.QM

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Abstract

Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical settings remains poorly assessed. This study evaluates the generalization capabilities of a CAD model for malaria diagnosis from thin blood smear images across four sites. We explore strategies to enhance generalization, including fine-tuning and incremental learning. Our results demonstrate that incorporating site-specific data significantly improves model performance, paving the way for broader clinical application.

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Cite this paper

@article{guillon2024assessing,
  title  = {Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears},
  author = {Louise Guillon and Soheib Biga and Axel Puyo and Grégoire Pasquier and Valentin Foucher and Yendoubé E. Kantchire and Stéphane E. Sossou and Ameyo M. Dorkenoo and Laurent Bonnardot and Marc Thellier and Laurence Lachaud and Renaud Piarroux},
  year   = {2024},
  eprint = {2408.08792},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.08792v1}
}

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