From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge
Open accessAbstract
Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen's kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.
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The best performing methods in CAMELYON employed convolutional neural networks differing in network architecture, training methods, and methods for pre- and post-processing.
“The best performing methods employed convolutional neural networks differing in network architecture, training methods, and methods for pre- and post- processing.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [171]
The CAMELYON challenges target methods for algorithmic detection and classification of breast cancer metastases in H&E whole slide lymph node sections.
“The CAMELYON challenges target methods for algorithmic detection and classification of breast cancer metastases in H&E whole slide lymph node sections [171].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [171]
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- Authors
- Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., Li, Q., Zanjani, F. G., Zinger, S., Fukuta, K., Komura, D., Ovtcharov, V., Cheng, S., Zeng, S., Thagaard, J., Dahl, A. B., Lin, H., Chen, H., Jacobsson, L., Hedlund, M., cetin, M., Halici, E., Jackson, H., Chen, R., Both, F., Franke, J., Kusters-Vandevelde, H., Vreuls, W., Bult, P., van Ginneken, B., van der Laak, J., & Litjens, G.
- Affiliations
- Radboud University Medical Center (NL)
- University Medical Center Utrecht (NL)
- Lunit (South Korea) (KR)
- Harvard University (US)
- Eindhoven University of Technology (NL)
- The University of Tokyo (JP)
- Tokyo Medical and Dental University (JP)
- Wuhan National Laboratory for Optoelectronics (CN)
- Technical University of Denmark (DK)
- Chinese University of Hong Kong (HK)
- ContextVision (Sweden) (SE)
- Middle East Technical University (TR)
- Karlsruhe Institute of Technology (DE)
- Canisius-Wilhelmina Ziekenhuis (NL)
- ORCID
- Bandi, Peter
- Geessink, Oscar
- Manson, Quirine
- Balkenhol, Maschenka
- Hermsen, Meyke
- Zhong, Aoxiao
- Li, Quanzheng
- Zanjani, Farhad Ghazvinian
- Zinger, Svitlana
- Komura, Daisuke
- Cheng, Shenghua
- Zeng, Shaoqun
- Thagaard, Jeppe
- Dahl, Anders B.
- Lin, Huangjing
- Chen, Hao
- Chen, Richard
- Vreuls, Willem
- Bult, Peter
- van der Laak, Jeroen
- Litjens, Geert
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2019-02
- Volume
- 38
- Issue
- 2
- Pages
- 550-560
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2889232360
- Cited by
- 569
- References
- 9
- Citations by year
- 2026: 86 · 2025: 97 · 2024: 92 · 2023: 74 · 2022: 78 · 2021: 65 · 2020: 47 · 2019: 28 · 2018: 1
- FWCI
- 19.84
- Type
- journal article
- Language
- en
- Open access
- green
- orbit.dtu.dk
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- TU/e Research Portal (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- lymph node metastasis detection, whole slide images, lymph node classification, isolated tumor cells, convolutional neural networks, ResNet, algorithm evaluation, algorithm combination, false positives, breast cancer
- MeSH terms
- Sentinel Lymph Node, Algorithms, Breast Neoplasms, Female, Histological Techniques, Humans, Image Interpretation, Computer-Assisted, Lymphatic Metastasis
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