Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach
This work addresses the challenge of deploying AI-based malaria diagnosis tools in low-resource settings by enhancing robustness to domain shifts, though it is incremental as it applies existing continual learning methods to a specific medical application.
The paper tackled the problem of limited generalization in deep learning-based malaria diagnosis systems across different sites by investigating continual learning strategies, finding that rehearsal-based methods significantly improved performance on a multi-site clinical dataset.
Malaria remains a major global health challenge, particularly in low-resource settings where access to expert microscopy may be limited. Deep learning-based computer-aided diagnosis (CAD) systems have been developed and demonstrate promising performance on thin blood smear images. However, their clinical deployment may be hindered by limited generalization across sites with varying conditions. Yet very few practical solutions have been proposed. In this work, we investigate continual learning (CL) as a strategy to enhance the robustness of malaria CAD models to domain shifts. We frame the problem as a domain-incremental learning scenario, where a YOLO-based object detector must adapt to new acquisition sites while retaining performance on previously seen domains. We evaluate four CL strategies, two rehearsal-based and two regularization-based methods, on real-life conditions thanks to a multi-site clinical dataset of thin blood smear images. Our results suggest that CL, and rehearsal-based methods in particular, can significantly improve performance. These findings highlight the potential of continual learning to support the development of deployable, field-ready CAD tools for malaria.