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arXiv · 2408.08792

Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

Louise Guillon·Soheib Biga·Axel Puyo·Grégoire Pasquier·Valentin Foucher·Yendoubé E. Kantchire·Stéphane E. Sossou·Ameyo M. Dorkenoo·Laurent Bonnardot·Marc Thellier·Laurence Lachaud·Renaud Piarroux
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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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Louise Guillon, Soheib Biga, Axel Puyo, Grégoire Pasquier, Valentin Foucher, Yendoubé E. Kantchire, Stéphane E. Sossou, Ameyo M. Dorkenoo, Laurent Bonnardot, Marc Thellier, Laurence Lachaud, Renaud Piarroux. 2024-08-16. Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears. https://doi.org/10.1007/978-3-031-82007-6_14

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