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For the HECKTOR 2025 challenge, evaluation will be conducted exclusively through Docker container submissions on the Grand Challenge platform. Docker Submission Overview

The task submission is divided into 1+2 phases:

  • Sanity Check Phase: Consists of 3 images to ensure participants are familiar with the Grand Challenge platform and that their dockers run without errors. All teams must make their submission to this phase and will receive feedback on any errors.
  • Validation Phase: Consists of approximately 50 images. All teams will submit up to 2 working dockers from the sanity check to this phase. Only the top 15 teams, as ranked by the evaluation metrics displayed on the public validation leaderboard, with valid submissions will proceed to Phase 3.
  • Testing Phase: Consists of approximately 400 images. The teams will choose 1 of their 2 dockers from the validation phase to be submitted to the testing phase. The official ranking of the teams will be based solely on the testing phase results.

The participants will not receive detailed feedback during the testing phase except for error notifications.

Task-Specific Submission RequirementsΒΆ

  1. Task 1: Docker containers should produce segmentation outputs as a single label mask per patient (1 for the predicted GTVp, 2 for GTVn, and 0 for the background) in .nii.gz format. The resolution of this mask should be the same as the original CT resolution. Participants should ensure correct pixel spacing and origin with respect to the original reference frame. The NIfTI files should be named [PatientID].nii.gz, matching the patient names, e.g., CHUB-001.nii.gz.
  2. Task 2: Docker containers should produce results as a JSON file containing the patient ID as "PatientID" and the output of the model (continuous) as "Prediction". The output should be anti-concordant with the RFS in days (i.e., the model should output a predicted risk score). For concordant outputs (e.g., predicted RFS days), simply submit your estimate times -1.
  3. Task 3: Docker containers should produce results as a JSON file containing the patient ID as "PatientID" and the output of the model (binary) as "Prediction". The output should be 1 for HPV positive and 0 for HPV negative.

Detailed documentation and code examples for Docker submissions will be made available on our GitHub repository to facilitate the process for all participants.