Dear all,

First of all, we would like to thank all of you for participating in the challenge . This was a big effort, and we are very glad that many people were interested in the challenge and worked very hard.  As mentioned in the challenge description, although this is a 'challenge', we see this as a collaboration where we work together to develop state-of-the-art methods to solve this clinically important problem. Whether your method is selected or not, your solution matters as it provides unique information to the research community. So thank you for being part of this.

As you know, the deadline to submit your solution to the final test set is on Jan 22. We hope that all of you would submit to the final test set since the selection of the methods for the overview paper will not only be based on the performance but also the methodology and the write-up. For that, I would like to mention a few important things.

  1. When you submit your solution to the final test set, you will need to upload a pdf file that explains your method. Please give enough details about the methodology that you have used. It should contain enough details about the architecture, preprocessing methods (if any), augmentation, training strategy (hyperparameters). 4-pages of paper should be sufficient. If the description is not clear, we might have to exclude the solution.

  2. For the generation track, we evaluate the methods by training a faster R-CNN model with the 1000 generated (fake nodule) images. In the overview paper, we will extend the experiments and will investigate the effect of the generated images on performance when they are combined with the real images. So, the evaluation metric that we calculate on grand-challenge is an approximation of 'how useful' the generated images are, but does not tell everything about it, of course. So, please submit your algorithm to this track even though the accuracy can be lower than the baseline method. If the solutions achieve a similar range of performance to the baseline (or higher, of course :) ), we will still give the awards to the best-performing solutions.

All the best with the final preparations, and please let us know if you have any questions.

Best, Ecem