Submission will be closed due to maintenance problems.
It will re-opened in May or June 2023.
This competition is part of the MICCAI 2019 Grand Challenge for Pathology.
Submission re-opened! (October 28, 2019) Challenge Results and leaderboard are announced (October 23, 2019) PAIP 2019 Program is now available. Check our details Here PAIP 2019 will be placed as a “Grand Pathology Challenge” at Intercontinental Hotel Shenzhen, Barcelona Room Test dataset released and submission start (September 02, 2019) Validation dataset released (August 12, 2019) Dataset, Rules, Evaluation page has been updated (July 25, 2019) Challenge schedule has been changed (July 17, 2019) Second training dataset released (May 20, 2019) First training dataset released (April 15, 2019)
The goal of the challenge is to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). There are two tasks and therefore two leaderboards which evaluate the performance of the algorithms for each task. Participants can choose to join both tasks or the Task1 only according to their interests.
Task 1: Liver Cancer SegmentationTask 2: Viable Tumor Burden Estimation
The liver is a visceral organ most often involved in the metastatic spread of cancer. For the best practice, early diagnosis of liver cancer is important but many people don't even know that they have hepatitis. Hepatocellular Carcinoma(HCC) represents about 90% of primary liver cancers and constitutes a major global health problem. The incidence of HCC is increasing both in Korea and worldwide; it is amongst the leading causes of cancer mortality globally. Between 1990 and 2015 newly diagnosed HCC cases increased by 75%, mainly due to changing age structures and population growth.
A tumor is composed of various cellular and stromal components, eg tumor cells, inflammatory cells, blood vessels, acellular matrix, tumor capsule, fluid, mucin, or necrosis. The viable tumor burden is defined as the ratio of viable tumor area to the whole area of the tumor. The need for evaluation of viable tumor burden is increasing, as an assessment of response rates for chemoradiotherapy or proportion of tumor cells in genetic testing using tissue samples. Traditional pathologists use a semiquantitative grading system for residual tumor burden or report portion of necrosis indirectly indicating viable tumor burden.
In the challenge, participants will be provided with 2 levels of data set extracted from Whole Slide Images.
- Tumor with prominent peritumoral reaction
- Tumor with minimal peritumoral or intratumoral reaction
The data and segmentation of the entire tumor area and viable tumor area are provided by Seoul National University Hospital, South Korea. The data are fully annotated by expert pathologists and are divided into 3 groups of data sets.
- The training data set contains 50 WSIs
- The validation data set contains 10 WSIs
- The test data set contains 40 WSIs
All WSIs were scanned at 20X magnification and all cases are randomly selected irrespective of the institutions.
The ground truth information of both tasks such as tumor segmentation is given to participants for the training set. For the validation and test set the ground, however, the truth information is reserved to the challenge committee and will be used to evaluate the performance of participant's AI learning models. (See the Detailed Data Description)
How to Participate
- Read the challenge rules carefully
- Register a grand-chllenge.org account
- Join the PAIP2019 challenge
- To download the dataset, visit here to fill and sign the DATA USE AND CONFIDENTIALITY AGREEMENT
- An e-mail with the link and access credentials to the dataset will be sent to your contact email.
If you have questions or comments, post a message on the forum. We usually respond to the questions within seven days. However, if you need any immediate assistance, please contact us at email@example.com, so that we can resolve any issues within 2-3 business days.