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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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Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

  • 会议论文

  • 发表日期：

    2025年

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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

摘要

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.