Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears
<|reference_start|>Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears: 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.<|reference_end|>
@article{guillon2024assessing,
    title={Assessing Generalization Capabilities of Malaria Diagnostic Models from
  Thin Blood Smears},
    author={Louise Guillon, Soheib Biga, Axel Puyo, Gr'egoire Pasquier, Valentin
  Foucher, Yendoub'e E. Kantchire, St'ephane E. Sossou, Ameyo M. Dorkenoo,
  Laurent Bonnardot, Marc Thellier, Laurence Lachaud, Renaud Piarroux},
    journal={arXiv preprint arXiv:2408.08792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08792},
    primaryClass={eess.IV cs.CV q-bio.QM}
}