• Every challenge participant agrees to use the provided data only in the scope of the DATA USE AND CONFIDENTIALITY AGREEMENT for access to data.
        • Every challenge participant agrees not to make more than one account for downloading data and submitting the results.       
        • After the release of the validation dataset, challenge participants may upload output results of their learning models as described in the submission guidelines of the challenge before the deadline including a summary of their own learning model. By submitting the participant’s results, every challenge participant confirms that their work is original and only includes material that they own or have permission from the rightful owner to use
        • By submitting the results to this challenge, participants understand and agree to include those results for the method comparison. This comparison will be used to prepare a joint publication in which the writing process will be led by the challenge organizer(s). First and last authorship position will correspond to the challenge organizer(s), and each participating team will have at least one contributing co-author in the author list.
        • Every challenge member agrees that the decisions of the challenge committee will be final and binding all matters related to this challenge. If there is any change to data, schedule, instructions of participation, or these rules, the registered participants will be notified to the email addresses they provided when they are registered.
        • If an unforeseen or unexpected event (including, but not limited to: someone cheating; a virus, bug, or catastrophic event corrupting data or the submission platform; someone discovering a flaw in the data or modalities of the challenge) that cannot be reasonably anticipated or controlled, (also referred to as force majeure) affects the fairness and/or integrity of this challenge, the committee reserve the right to cancel, change or suspend this challenge. This right is reserved whether the event is due to human or technical error.
        • Computer “hacking” is unlawful. If any participant attempts to compromise the integrity or the legitimate operation of the challenge by hacking or by cheating or committing fraud in any way, the committee may seek damages from him/her to the fullest extent permitted by law.
        • For training the network, no external data is allowed except the official dataset provided in this challenge and pre-trained models using the ImageNet database such as VGG16, InceptionV3, ResNet, etc. 


        • 1st  $1,000
        • 2nd $500

        The top 10 teams will have opportunity to present their result at the KOSOMBE autumn conference which is scheduled November 12th - 13th 2020.


        High ranked teams will be suggested to submit papers to Health Informatics Research (HIR, which is a SCOPUS citation journal. The submission is optional, but the HIR journal will provide a fast track review.


        The PAIP2020 dataset is available under the Creative Commons Attribution-Non Commercial 4.0 International License (CC BY-NC 4.0).
        Under the following terms:

        Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the license endorses you or your use.
        Non-Commercial — You may not use the material for commercial purposes.
        No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.


        Please acknowledge PAIP2020 in your publications as follows:

        "De-identified pathology images and annotations used in this research were prepared and provided by the Seoul National University Hospital by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C0316).”