About PAIP

The Pathology Artificial Intelligence Platform (PAIP) is a research platform established to support the development and evaluation of artificial intelligence methods in digital pathology.

PAIP Platform Dataset

The PAIP platform dataset will be made available through external links via the Korea Health Data Platform (KHDP) of Seoul National University Hospital.

The dataset is currently being prepared for transfer to the platform. Additional information and access instructions will be updated once the dataset becomes available.

The dataset contains approximately 3000 whole-slide images (WSIs) with pathologist annotations across five cancer types:

The information will be updated when the dataset becomes available.

PAIP Challenge Data Access

The datasets are made available under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) .

Please note that only the PAIP 2019 and PAIP 2020 datasets are currently available. The available datasets vary by challenge. Before submitting a Data Use Agreement (DUA), please make sure to check the challenge name and dataset description on the corresponding official challenge website. Links to the official challenge websites can be found in the Challenges section below.

PAIP challenge datasets are provided for research use after review and approval of a DUA.

Once a DUA has been approved by the PAIP team, temporary access to the requested dataset will be provided. Therefore, if you have submitted a DUA, please check your email regularly for access information.

To request access, please complete the DUA available from the link below and send the completed document by email.

Download Data Use Agreement

Email: lab.kblee117@gmail.com

PAIP Challenges

PAIP 2019 Liver Cancer Segmentation Challenge

PAIP 2019

Liver Cancer Segmentation Challenge

Tasks included liver cancer segmentation and viable tumor burden estimation in whole-slide pathology images.

Official PAIP 2019 Challenge Website

PAIP 2020 MSI Prediction in Colorectal Cancer

PAIP 2020

Microsatellite Instability Prediction in Colorectal Cancer

The challenge focused on predicting microsatellite instability (MSI) status from colorectal cancer whole-slide images.

Official PAIP 2020 Challenge Website

PAIP 2021 Perineural Invasion in Multiple Organ Cancer

PAIP 2021

Perineural Invasion in Multiple Organ Cancer (Colon, Prostate and Pancreatobiliary tract)

The challenge focused on detecting perineural invasion in colon, prostate, and pancreatobiliary cancers.

Official PAIP 2021 Challenge Website

PAIP 2023 Tumor Cellularity Prediction Challenge

PAIP 2023

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

The challenge addressed tumor cellularity prediction in pancreatic cancer using supervised learning and in colorectal cancer using transfer learning.

Official PAIP 2023 Challenge Website

How to Cite PAIP Datasets

If you use a PAIP challenge dataset in a publication, please cite the corresponding challenge publication or official challenge website as indicated below.

PAIP 2019

Kim YJ, Jang H, Lee K, et al. PAIP 2019: Liver cancer segmentation challenge. Medical Image Analysis. 2021;67:101854.

DOI: 10.1016/j.media.2020.101854

Show full citation and author list

Yoo Jung Kim, Hyungjoon Jang, Kyoungbun Lee, Seongkeun Park, Sung-Gyu Min, Choyeon Hong, Jeong Hwan Park, Kanggeun Lee, Jisoo Kim, Wonjae Hong, Hyun Jung, Yanling Liu, Haran Rajkumar, Mahendra Khened, Ganapathy Krishnamurthi, Sen Yang, Xiyue Wang, Chang Hee Han, Jin Tae Kwak, Jianqiang Ma, Zhe Tang, Bahram Marami, Jack Zeineh, Zixu Zhao, Pheng Ann Heng, Rüdiger Schmitz, Frederic Madesta, Thomas Rösch, Rene Werner, Jie Tian, Elodie Puybareau, Matteo Bovio, Xiufeng Zhang, Yifeng Zhu, Se Young Chun, Won-Ki Jeong, Peom Park, Jinwook Choi. PAIP 2019: Liver cancer segmentation challenge. Medical Image Analysis. 2021;67:101854.

PAIP 2020

Kim K, Lee K, Cho S, et al. PAIP 2020: Microsatellite instability prediction in colorectal cancer. Medical Image Analysis. 2023;89:102886.

DOI: 10.1016/j.media.2023.102886

Show full citation and author list

Kyungmo Kim, Kyoungbun Lee, Sungduk Cho, Dong Un Kang, Seongkeun Park, Yunsook Kang, Hyunjeong Kim, Gheeyoung Choe, Kyung Chul Moon, Kyu Sang Lee, Jeong Hwan Park, Choyeon Hong, Ramin Nateghi, Fattaneh Pourakpour, Xiyue Wang, Sen Yang, Seyed Alireza Fatemi Jahromi, Aliasghar Khani, Hwa-Rang Kim, Doo-Hyun Choi, Chang Hee Han, Jin Tae Kwak, Fan Zhang, Bing Han, David Joon Ho, Gyeong Hoon Kang, Se Young Chun, Won-Ki Jeong, Peom Park, Jinwook Choi. PAIP 2020: Microsatellite instability prediction in colorectal cancer. Medical Image Analysis. 2023;89:102886.

PAIP 2021

Please cite the official PAIP 2021 challenge website until the journal publication becomes available.

PAIP 2021 Challenge: Perineural Invasion in Multiple-Organ Cancer

The journal publication associated with PAIP 2021 will be added here once available.

PAIP 2023

Please cite the official PAIP 2023 challenge website until the journal publication becomes available.

PAIP 2023: Tumor Cellularity Prediction in Pancreatic and Colorectal Cancer

The journal publication associated with PAIP 2023 will be added here once available.

Acknowledgments

This research was supported 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). Ethics approval: Approved by Seoul National University Hospital Institutional Review Board (IRB) (IRB No.H-1808-035-964).

Detailed ethics approval and consent information should be referred to in the corresponding dataset documentation or publication.

Contact

For questions regarding PAIP datasets or data access:

lab.kblee117@gmail.com