Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

Autor:	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
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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. Comment: MICCAI 2024 AMAI Workshop, Accepted for presentation, Submitted Manuscript Version, 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.08792 Zobrazit plný text záznamu View this record from Arxiv