# Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach

arXiv: https://arxiv.org/abs/2507.23648 · HTML: https://mlsanity.com/paper/2507.23648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology](https://mlsanity.com/paper/2404.05022/) (2024)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Applying Faster R-CNN for Object Detection on Malaria Images](https://mlsanity.com/paper/1804.09548/) (2018)
- [A Continual Learning Approach for Cross-Domain White Blood Cell Classification](https://mlsanity.com/paper/2308.12679/) (2023)
- [Continual Learning with Strong Experience Replay](https://mlsanity.com/paper/2305.13622/) (2023)
- [CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes](https://mlsanity.com/paper/2402.10478/) (2024)
- [Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears](https://mlsanity.com/paper/2408.08792/) (2024)
- [Addressing Challenges in Data Quality and Model Generalization for Malaria Detection](https://mlsanity.com/paper/2501.00464/) (2024)
- [Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection](https://mlsanity.com/paper/2503.04688/) (2025)

## Abstract (arXiv)

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.