02101nas a2200229   4500000000100000008004100001260001200042653002800054653003400082653001700116653001900133653002300152653003000175100001400205700004300219700001900262700002000281245008500301856003700386300000900423520143900432       2025                            d  c12/202510aArtificial Intelligence10aHistopathology image analysis10asegmentation10aClassification10aMycetoma diagnosis10a Computer-aided diagnosis1 aHyam Ali 1 aSahar Salah Aldeen Mohammed Alhesseen 1 aLamis Elkheir 1 aAdrian Galdran 00aAI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge  uhttps://arxiv.org/pdf/2512.21792  a1-213 a<p>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.</p>