PAIP 2019: Liver cancer segmentation challenge
Open accessAbstract
Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.
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CTransPath is a lightweight (30 million parameters) Swin Transformer trained at a resolution of 1.0 micron per pixel on over 32,000 WSIs from TCGA and PAIP using a semantically guided contrastive learning algorithm.
“The former is a lightweight (30 million parameters) Swin Transformer15 trained at a resolution of 1.0 micron per pixel (mpp) on over 32,000 WSIs from TCGA16 and PAIP19 using a semantically guided contrastive learning algorithm20.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [19]
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- Authors
- Kim, Y. J., Jang, H., Lee, K., Park, S., Min, S.-G., Hong, C., Park, J. H., Lee, K., Kim, J., Hong, W., Jung, H., Liu, Y., Rajkumar, H., Khened, M., Krishnamurthi, G., Yang, S., Wang, X., Han, C. H., Kwak, J. T., Ma, J., Tang, Z., Marami, B., Zeineh, J., Zhao, Z., Heng, P.-A., Schmitz, R., Madesta, F., Rösch, T., Werner, R., Tian, J., Puybareau, E., Bovio, M., Zhang, X., Zhu, Y., Chun, S. Y., Jeong, W.-K., Park, P., & Choi, J.
- Affiliations
- Seoul National University (KR)
- Ulsan National Institute of Science and Technology (KR)
- Seoul National University Hospital (KR)
- Frederick National Laboratory for Cancer Research (US)
- Indian Institute of Technology Madras (IN)
- Sichuan University (CN)
- Sejong University (KR)
- Alibaba Group (China) (CN)
- Icahn School of Medicine at Mount Sinai (US)
- Chinese University of Hong Kong (HK)
- University Medical Center Hamburg-Eppendorf (DE)
- Shanghai Jiao Tong University (CN)
- École Pour l'Informatique et les Techniques Avancées (FR)
- Tianjin Chengjian University (CN)
- University of Maine (US)
- ORCID
- Kim, Yoo Jung
- Jang, Hyungjoon
- Park, Seongkeun
- Hong, Choyeon
- Park, Jeong Hwan
- Lee, Kanggeun
- Kim, Jisoo
- Hong, Wonjae
- Jung, Hyun
- Liu, Yanling
- Khened, Mahendra
- Krishnamurthi, Ganapathy
- Yang, Sen
- Wang, Xiyue
- Han, Chang Hee
- Kwak, Jin Tae
- Ma, Jianqiang
- Tang, Zhe
- Marami, Bahram
- Zhao, Zixu
- Heng, Pheng-Ann
- Schmitz, Rüdiger
- Madesta, Frederic
- Rösch, Thomas
- Zhang, Xiufeng
- Zhu, Yifeng
- Chun, Se Young
- Jeong, Won-Ki
- Choi, Jinwook
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 67
- Pages
- 101854
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W3092103057
- Cited by
- 131
- References
- 32
- Citations by year
- 2026: 21 · 2025: 30 · 2024: 25 · 2023: 22 · 2022: 20 · 2021: 12 · 2020: 1
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- Korea Health Industry Development Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- liver cancer, whole slide images, digital pathology, benchmarking, automated cancer detection
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Liver Neoplasms, Tumor Burden
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