Tasks

Participants are tasked with developing algorithms that can effectively detect and classify Mycetoma from histopathological images.

Objective: Develop an algorithm capable of accurately detecting the presence of Mycetoma grains within the provided histopathological images.
Background: Mycetoma grains are indicative of the disease, and their accurate detection is crucial for correct diagnosis and subsequent treatment planning.
Output Specification: Algorithms should output the boundary definitions of each detected Mycetoma grain within the images.

Objective: Classify detected Mycetoma grains into one of two categories: Actinomycetoma (caused by bacteria) or Eumycetoma (caused by fungi).
Background: The type of Mycetoma significantly affects treatment decisions. Actinomycetoma is generally treated with antibiotics, while Eumycetoma often requires antifungal medications and sometimes surgical intervention.
Output Specification: For each detected Mycetoma grain, the algorithm should output a classification label indicating whether it is Actinomycetoma or Eumycetoma.

Participants are encouraged to utilize the training data provided to develop their detection and classification models. While additional external data and/or pre-trained models can be incorporated, transparency in their use is required for submission evaluation.

Development Environment: Participants may choose their development environment and tools but must ensure that their submission is compatible with the evaluation platform specified by the challenge organizers.
Interpretability: While not a requirement, the ability to explain decisions made by the algorithm will be viewed favourably, as it enhances trust in automated systems by clinicians.
Participants are encouraged to share their codes in the public repositories.