Towards Low-Cost and Efficient Malaria Detection

TL;DR

This paper introduces a large-scale, publicly available dataset for malaria detection using low-cost microscopes, along with domain adaptation methods to improve diagnostic accuracy at low magnification.

Contribution

It provides a novel dataset for malaria microscopy at low magnification and proposes domain adaptation techniques to transfer annotations from high-cost to low-cost microscopes.

Findings

Domain adaptation improves detection accuracy on low-cost microscope images.

Partially supervised methods outperform fully supervised baselines.

The dataset enables research on low-cost malaria diagnostics.

Abstract

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.