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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2512.21792 |
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| author | Ali, Hyam Omar Alhesseen, Sahar Elkhair, Lamis Galdran, Adrian Feng, Ming Xiong, Zhixiang Lin, Zengming Xu, Kele Hu, Liang Keel, Benjamin Mills, Oliver Battye, James Kumar, Akshay Aslam, Asra Dutande, Prasad Baid, Ujjwal Baheti, Bhakti Gajre, Suhas Murali, Aravind Shrenivas Lee, Eung-Joo Fahal, Ahmed Jennane, Rachid |
| author_facet | Ali, Hyam Omar Alhesseen, Sahar Elkhair, Lamis Galdran, Adrian Feng, Ming Xiong, Zhixiang Lin, Zengming Xu, Kele Hu, Liang Keel, Benjamin Mills, Oliver Battye, James Kumar, Akshay Aslam, Asra Dutande, Prasad Baid, Ujjwal Baheti, Bhakti Gajre, Suhas Murali, Aravind Shrenivas Lee, Eung-Joo Fahal, Ahmed Jennane, Rachid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge Ali, Hyam Omar Alhesseen, Sahar Elkhair, Lamis Galdran, Adrian Feng, Ming Xiong, Zhixiang Lin, Zengming Xu, Kele Hu, Liang Keel, Benjamin Mills, Oliver Battye, James Kumar, Akshay Aslam, Asra Dutande, Prasad Baid, Ujjwal Baheti, Bhakti Gajre, Suhas Murali, Aravind Shrenivas Lee, Eung-Joo Fahal, Ahmed Jennane, Rachid Computer Vision and Pattern Recognition 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. |
| title | AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.21792 |