# Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears

arXiv: https://arxiv.org/abs/2408.08792 · HTML: https://mlsanity.com/paper/2408.08792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Applying Faster R-CNN for Object Detection on Malaria Images](https://mlsanity.com/paper/1804.09548/) (2018)
- [Towards Low-Cost and Efficient Malaria Detection](https://mlsanity.com/paper/2111.13656/) (2021)
- [Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information](https://mlsanity.com/paper/1908.01901/) (2019)
- [CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes](https://mlsanity.com/paper/2402.10478/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach](https://mlsanity.com/paper/2507.23648/) (2025)

## Abstract (arXiv)

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.