AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge
Hyam Omar Ali, Sahar Alhesseen, Lamis Elkhair, Adrian Galdran, Ming Feng, Zhixiang Xiong, Zengming Lin, Kele Xu, Liang Hu, Benjamin Keel, Oliver Mills, James Battye, Akshay Kumar, Asra Aslam, Prasad Dutande, Ujjwal Baid, Bhakti Baheti, Suhas Gajre, Aravind Shrenivas Murali

TL;DR
This paper discusses the MICCAI 2024 challenge focused on developing AI models for automated mycetoma diagnosis from histopathological images, highlighting high accuracy in segmentation and classification tasks.
Contribution
It introduces a standardized dataset and benchmark for AI-based mycetoma diagnosis, fostering progress in automated detection and classification.
Findings
High segmentation accuracy achieved by models
Significant performance in mycetoma type classification
Validation of AI models for low-resource settings
Abstract
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…
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Taxonomy
TopicsActinomycetales infections and treatment · AI in cancer detection · Lung Cancer Diagnosis and Treatment
