Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in low-resource settings where expert pathologists are limited. To address this challenge, this paper presents an overview of the Mycetoma MicroImage: Detect and Classify Challenge (mAIcetoma) which was organized to advance mycetoma diagnosis through AI solutions. mAIcetoma focused on developing automated models for segmenting mycetoma grains and classifying mycetoma types from histopathological images. The challenge attracted the attention of several teams worldwide to participate and five finalist teams fulfilled the challenge objectives. The teams proposed various deep learning architectures for the ultimate goal of this challenge. Mycetoma database (MyData) was provided to participants as a standardized dataset to run the proposed models. Those models were evaluated using evaluation metrics. Results showed that all the models achieved high segmentation accuracy, emphasizing the necessitate of grain detection as a critical step in mycetoma diagnosis. In addition, the top-performing models show a significant performance in classifying mycetoma types.

翻译：足菌肿是一种被忽视的热带疾病，由真菌或细菌引起，可导致严重的组织损伤和残疾。该疾病影响贫困和农村社区，给全球流行地区的患者及医疗系统带来医学挑战和社会经济负担。足菌肿诊断是疾病管理中的主要挑战，在病理学专家资源有限的低收入地区尤为突出。为应对这一挑战，本文概述了旨在通过AI解决方案推进足菌肿诊断的"足菌肿显微图像检测与分类挑战赛（mAIcetoma）"。该挑战赛聚焦于开发从组织病理学图像中分割足菌肿颗粒并分类足菌肿类型的自动化模型。挑战赛吸引了全球多支团队参与，最终有五支决赛团队完成了挑战目标。各团队为达成挑战的最终目标提出了多种深度学习架构。赛事向参与者提供了标准化数据集——足菌肿数据库（MyData），用于运行所提出的模型。这些模型通过评估指标进行性能评估。结果表明，所有模型均实现了较高的分割精度，强调了颗粒检测作为足菌肿诊断关键步骤的必要性。此外，表现最优的模型在足菌肿类型分类方面展现出显著性能。