To participate, each participants will have to submit runs for the following two tasks:
Submissions should be submitted in separated files with the following names, depending on the subtask:
To submit, please zip the submission files and send the zip archive via regular e-mail to the task organizers Pål Halvorsen (paalh at simula.no), Steven Hicks (steven at simula.no) and Michael Riegler (Michael at simula.no).
More details on how to submit for the specific tasks can be found found below.
Information on how to prepare the Docker image and submissions can be found here.
Submissions to the detection task should be a .CSV file containing the the image name, classification label, and confidence value. The .CSV file should be generated using the test dataset.Ranking Submissions to the detection task will be ranekd based on the achieved MCC on the test dataset. In all the submissions the confidence value/interestingness level/rank value refers to rank orders, expressing an expectation that some classified instances are more likely to be positive / correctly classified than others. The rank value is expected to be a floating-point value within [0.0;1.0] interval deciding for any pair of instances whether the first is more likely (>), equally likely (=), or less likely (<) to be positive / correctly classified than the second. If your algorithm does not provide any ranking for the classified instances, you must use 1.0 as the rank value.
Submissions to the efficient detection task are similar to the detection task, but also include information about processing time. The .CSV file should contain the image name, classification label, confidence value, image processing time in seconds. The .CSV file should be generated by a Docker image containing your code and takes the test dataset as input.Ranking Submissions ot the Efficient detection task will be ranked based on their processing time in seconds and achieved MCC. The image processing time in seconds value refers to a time interval from the moment when image has been completely loaded into memory to the moment of the final decision about the image class made by the classification algorithm. In other words, the full amount of time required to classify a particular image minus time spent to image loading and classification results saving.
Submissions to the segmentation task should be a single zip file for all the predicted mask. The name of the predicted mask image should be the same as the input image.Ranking Submissions to the segmentation task will be ranked based on their achieved DICE score on the provided test dataset.