# Towards Low-Cost and Efficient Malaria Detection

arXiv: https://arxiv.org/abs/2111.13656 · HTML: https://mlsanity.com/paper/2111.13656/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://mlsanity.com/paper/1803.03243/) (2018)
- [Strong-Weak Distribution Alignment for Adaptive Object Detection](https://mlsanity.com/paper/1812.04798/) (2018)
- [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://mlsanity.com/paper/2009.12829/) (2020)
- [Cross-domain Detection via Graph-induced Prototype Alignment](https://mlsanity.com/paper/2003.12849/) (2020)
- [Applying Faster R-CNN for Object Detection on Malaria Images](https://mlsanity.com/paper/1804.09548/) (2018)
- [Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge](https://mlsanity.com/paper/2102.13644/) (2021)
- [A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images](https://mlsanity.com/paper/2102.08708/) (2021)

## Built on by (most cited first, 12 of 12)

- [Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment](https://mlsanity.com/paper/2405.14497/) (2024)
- [Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning](https://mlsanity.com/paper/2311.04815/) (2023)
- [A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability](https://mlsanity.com/paper/2405.10803/) (2024)
- [CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes](https://mlsanity.com/paper/2402.10478/) (2024)
- [Few-Shot Domain Adaptive Object Detection for Microscopic Images](https://mlsanity.com/paper/2407.07633/) (2024)
- [RedDino: A foundation model for red blood cell analysis](https://mlsanity.com/paper/2508.08180/) (2025)
- [Uni-Hema: Unified Model for Digital Hematopathology](https://mlsanity.com/paper/2511.13889/) (2025)
- [MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities](https://mlsanity.com/paper/2511.20650/) (2025)
- [Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears](https://mlsanity.com/paper/2408.08792/) (2024)
- [Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset](https://mlsanity.com/paper/2504.02602/) (2025)
- [AdaptiveCDM: Source-Free Few-Shot Domain Adaptation for Cell Detection in Microscopic Images](https://mlsanity.com/paper/2503.03370/) (2025)
- [A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears](https://mlsanity.com/paper/2507.18483/) (2025)

## Abstract (arXiv)

Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over the low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood-smear slides from several malaria-infected patients, collected through microscopes at two different cost spectrums and multiple magnifications. Malarial cells are annotated for the localization and life-stage classification task on the images collected through the high-cost microscope at high magnification. We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications. Multiple object detectors and domain adaptation methods are presented as the baselines. Furthermore, a partially supervised domain adaptation method is introduced to adapt the object-detector to work on the images collected from the low-cost microscope. The dataset will be made publicly available after publication.