Malaria Detection
Rank: 1st out of 287
Annotating protein complexes in 3D cellular images to accelerate biomedical discoveries and disease treatment
About the competition
Spotting the early stages of malaria parasites in blood samples is a challenge that can make the difference between life and death. Early and accurate diagnosis is essential for effective treatment and management of the disease.
The goal of this competition is to train an AI model to identify malaria parasites in blood slide images using a dataset of annotated samples from Uganda and Ghana. These local datasets ensure that the model is tailored for the blood types of these areas, minimizing diagnostic biases and improving detection accuracy.
Lacuna Fund
Lacuna Fund mobilizes funding to create high-quality labeled datasets that address challenges in low- and middle-income countries worldwide. By supporting data scientists, researchers, and social entrepreneurs, Lacuna Fund provides the necessary resources to either create new datasets that address underserved populations or critical issues, enhance existing datasets to ensure greater representation, or update outdated datasets.
Lacuna Fund datasets are locally developed, owned, and openly accessible to the international community, while ensuring practices regarding ethics and privacy.
Relevance
Malaria is a major public health challenge in Africa, causing hundreds of thousands of deaths each year, particularly affecting pregnant women and children under five. Early detection and treatment are essential to prevent severe health consequences. In regions like Uganda and Ghana, traditional diagnostic methods are resource-intensive and demand skilled technicians, both of which are often in short supply.
This competition aims to develop an AI model that can automate and ease the process of malaria detection, offering early diagnosis. Which ensures that patients receive timely treatment, potentially saving lives.
Technical Details
The dataset for this competition focuses on detecting and classifying malaria parasites in blood samples, which were captured using phone cameras, adding to the challenge. The goal is to develop a multiclass object detection model that can accurately identify the trophozoite stage of malaria and differentiate between infected and uninfected blood cells. The presence of other elements, such as larger white blood cells that also need to be annotated, and image artifacts such as smudging, further complicates the task and increases the risk of false positives.
The team is researching multiple types of object detection models. Since the dataset contains images of different resolutions and has many other variations, it is important that the team can develop a model which can generalize well.
UN Sustainable Development Goals
This competition contributes to the UN Sustainable Development Goal #3: Good Health and Well-Being. By participating, we are advancing early-stage malaria detection to ensure timely and accurate diagnoses, particularly in underserved regions, improving access to quality healthcare.
