Mycetoma MicroImage Challenge: Final Results

 

The Mycetoma MicroImage: Detect and Classify Challenge brought together global experts to tackle a significant challenge in mycetoma diagnosis. The challenge aimed to develop AI-driven, fully automated models for detecting and classifying mycetoma from histopathological images, with the potential to improve diagnostic accuracy and treatment outcomes.

The challenge attracted participation from teams across the globe. After a rigorous evaluation process, five finalist teams were selected:

  • Adrian Galdran (Computer Vision Center, UAB, Spain)
  • VSI LAB (University of Arizona, United States)
  • Minions (University of Leeds, United Kingdom)
  • Macaroon (Tongji University, China)
  • Tiger (Indiana University, United States)

 

Winners

  1. Top Performer: Adrian Galdran
  2. Runner-Up: Team Macaroon
  3. Third Place: Team Minions and Team Tiger

Congratulations to all participants!

 

Following the success of the Mycetoma MicroImage Challenge, we aim to further the impact of the developed AI models by integrating them into desktop and mobile applications for clinical use, particularly in low-resource settings where mycetoma is most prevalent. These applications will help improve diagnostic accuracy and accessibility. Additionally, we plan to collaborate with healthcare providers, AI researchers, and public health experts to refine the tools and support their adoption in regions most affected by mycetoma.

We are excited about the potential of these models to revolutionise mycetoma diagnosis and are actively planning future collaborations to bring these solutions into the field. Stay tuned for more updates!