Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911085079035904 |
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| author | Guillon, Louise Biga, Soheib Puyo, Axel Pasquier, Grégoire Foucher, Valentin Kantchire, Yendoubé E. Sossou, Stéphane E. Dorkenoo, Ameyo M. Bonnardot, Laurent Thellier, Marc Lachaud, Laurence Piarroux, Renaud |
| author_facet | Guillon, Louise Biga, Soheib Puyo, Axel Pasquier, Grégoire Foucher, Valentin Kantchire, Yendoubé E. Sossou, Stéphane E. Dorkenoo, Ameyo M. Bonnardot, Laurent Thellier, Marc Lachaud, Laurence Piarroux, Renaud |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears Guillon, Louise Biga, Soheib Puyo, Axel Pasquier, Grégoire Foucher, Valentin Kantchire, Yendoubé E. Sossou, Stéphane E. Dorkenoo, Ameyo M. Bonnardot, Laurent Thellier, Marc Lachaud, Laurence Piarroux, Renaud Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2408.08792 |