CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes

arXiv:2402.10478v1 Announce Type: cross
Abstract: Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM). For this reason, a practical solution …

abstract arxiv cells computer cost cs.cv cs.lg deep learning detection diagnosis domain domain adaptation health images issue low major malaria scalable show solutions success type work