The Camelyon16 ISBI challenge took place on Wednesday, 13 April. The presentations from the organizing team are now available. Camelyons16 was closed in November 2016 following the launch of Camelyon17.
| Title of presentation | Presenter | Download |
| How computers shape the future of pathology | Jeroen van der Laak |  |
| Camelyon16: Aim, dataset, and evaluation | Babak Ehteshami Bejnordi | |
| Statistics, Leaderboards, Results and Comparison to Pathologist | Babak Ehteshami Bejnordi | |
Public Leaderboard 1 - Whole-slide-image classification
- The results are computed on the independent test set.
- Evaluation 1: Teams are ranked based on area under ROC curve (AUC).
Top-five ranked teams until the challenge event deadline (Apr 1, 2016):
| Rank | Team | AUC | Submission date | Description |
| 01 | Harvard Medical School and MIT, Method 1 | 0.9234 | 01 Apr 2016 |  |
| 02 | EXB Research and Development co., Germany | 0.9156 | 01 Apr 2016 | |
| 03 | Independent participant, Germany | 0.8654 | 01 Apr 2016 | |
| 04 | Middle East Technical University, Departments of EEE, NSNT and HS, Turkey | 0.8642 | 01 Apr 2016 | |
| 05 | NLP LOGIX co., USA | 0.8298 | 01 Apr 2016 | |
Leaderboard including all submissions
* Indicates that the team has achieved an AUC value that surpasses the AUC of the pathologist in our study.
| Rank | Team | AUC | Submission date | Description |
| 01 * | Harvard Medical School and MIT, Method 2 (updated) | 0.9935 | 06 Nov 2016 | |
| 02 * | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 3 | 0.9763 | 24 Oct 2016 | |
| 03 | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 1 | 0.9650 | 07 Sep 2016 | |
| 04 | The Chinese University of Hong Kong (CU lab, Hong Kong), Method 3 | 0.9415 | 29 Aug 2016 | |
| 05 | Harvard Medical School and MIT, Method 1 | 0.9234 | 01 Apr 2016 | |
| 06 | EXB Research and Development co., Germany | 0.9156 | 01 Apr 2016 | |
| 07 | The Chinese University of Hong Kong (CU lab), Hong Kong, Method 1 | 0.9086 | 08 June 2016 | |
| 08 | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 2 | 0.9082 | 24 Oct 2016 | |
| 09 | The Chinese University of Hong Kong (CU lab), Hong Kong, Method 2 | 0.9056 | 20 July 2016 | |
| 10 | DeepCare Inc, China | 0.8833 | 05 Nov 2016 | |
| 11 | Independent participant, Germany | 0.8654 | 01 Apr 2016 | |
| 12 | Middle East Technical University, Departments of EEE, NSNT and HS, Turkey | 0.8642 | 01 Apr 2016 | |
| 13 | NLP LOGIX co., USA | 0.8298 | 01 Apr 2016 | |
| 14 | Smart Imaging Technologies co., USA | 0.8207 | 14 May 2016 | |
| 15 | University of Toronto, Electrical and Computer Engineering, Canada | 0.8149 | 01 Apr 2016 | |
| 16 | The Warwick-QU Team, United Kingdom | 0.7958 | 01 Apr 2016 | |
| 17 | Radboud University Medical Center (DIAG), Netherlands | 0.7786 | 01 Apr 2016 |
|
| 18 | HTW-BERLIN, Germany | 0.7676 | 01 Apr 2016 | |
| 19 | University of Toronto, Electrical and Computer Engineering, Canada | 0.7621 | 01 Apr 2016 | |
| 20 | BioMediTech, University of Tampere, Finland | 0.7612 | 01 Apr 2016 | |
| 21 | Smart Imaging Technologies co., USA | 0.7574 | 01 Apr 2016 | |
| 22 | Technical University of Munich (CAMP), Germany - Method 2 | 0.7367 | 30 Aug 2016 | |
| 23 | Osaka University, Department of Bioinformatic Engineering, Japan | 0.7319 | 01 Apr 2016 | |
| 24 | University of South Florida, Computer Science and Engineering, USA | 0.7270 | 01 Apr 2016 | |
| 25 | NSS college of Engineering, India | 0.7269 | 01 Apr 2016 | |
| 26 | BioMediTech, University of Tampere, Finland | 0.7132 | 01 Apr 2016 | |
| 27 | Technical University of Munich (CAMP), Germany | 0.6910 | 01 Apr 2016 | |
| 28 | United Institute of Informatics Problems, Belarus | 0.6890 | 01 Apr 2016 | |
| 29 | VISILAB, University of Castilla-La Mancha, Spain | 0.6531 | 01 Apr 2016 | |
| 30 | VISILAB, University of Castilla-La Mancha, Spain | 0.6513 | 01 Apr 2016 | |
| 31 | Mines Paris Tec, France | 0.6277 | 01 Apr 2016 | |
| 32 | Sorbonne Universites, Laboratoire d’Imagerie Biomdicale, France | 0.5561 | 01 Apr 2016 |
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Public Leaderboard 2 - Tumor localization
- The results are computed on the independent test set.
- Evaluation 2: The detection/localization performance is summarized using Free Response Operating Characteristic (FROC) curves. The final score is defined as the average sensitivity at 6 predefined false positive rates: 1/4, 1/2, 1, 2, 4, and 8 FPs per whole slide image.
Top-five ranked teams until the challenge event deadline (Apr 1, 2016):
| Rank | Team | score | Submission date | Description |
| 01 | Harvard Medical School and MIT, Method 1 | 0.6933 | 01 Apr 2016 | |
| 02 | Radboud University Medical Center (DIAG), Netherlands | 0.5748 | 01 Apr 2016 | |
| 03 | EXB Research and Development co., Germany | 0.5111 | 01 Apr 2016 | |
| 04 | Middle East Technical University, Departments of EEE, NSNT and HS, Turkey | 0.3889 | 01 Apr 2016 | |
| 05 | NLP LOGIX co., USA | 0.3859 | 01 Apr 2016 | |
Leaderboard including all submissions
| Rank | Team | score | Submission date | Description |
| 01 | Harvard Medical School and MIT, Method 2 (updated) | 0.8074 | 06 Nov 2016 | |
| 02 | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 3 | 0.7600 | 24 Oct 2016 | |
| 03 | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 2 | 0.7289 | 24 Oct 2016 | |
| 04 | The Chinese University of Hong Kong (CU lab), Method 3 | 0.7030 | 29 Aug 2016 | |
| 05 | Harvard Medical School and MIT, Method 1 | 0.6933 | 01 Apr 2016 | |
| 06 | Harvard Medical School, Gordon Center for Medical Imaging, MGH, Method 1 | 0.5963 | 07 Sep 2016 | |
| 07 | Radboud University Medical Center (DIAG), Netherlands | 0.5748 | 01 Apr 2016 | |
| 08 | The Chinese University of Hong Kong (CU lab) - Method 1 | 0.5444 | 08 June 2016 | |
| 09 | The Chinese University of Hong Kong (CU lab) - Method 2 | 0.5274 | 20 July 2016 | |
| 10 | EXB Research and Development co., Germany | 0.5111 | 01 Apr 2016 | |
| 11 | Middle East Technical University, Departments of EEE, NSNT and HS, Turkey | 0.3889 | 01 Apr 2016 | |
| 12 | NLP LOGIX co., USA | 0.3859 | 01 Apr 2016 | |
| 13 | University of Toronto, Electrical and Computer Engineering, Canada | 0.3822 | 01 Apr 2016 | |
| 14 | Independent participant, Germany | 0.3667 | 01 Apr 2016 | |
| 15 | University of Toronto, Electrical and Computer Engineering, Canada | 0.3519 | 01 Apr 2016 | |
| 16 | Osaka University, Department of Bioinformatic Engineering, Japan | 0.3467 | 01 Apr 2016 | |
| 17 | Smart Imaging Technologies, USA | 0.3385 | 14 May 2016 | |
| 18 | The Warwick-QU Team, United Kingdom | 0.3052 | 01 Apr 2016 | |
| 19 | Technical University of Munich (CAMP), Germany, Method 2 | 0.2733 | 30 Aug 2016 | |
| 20 | BioMediTech, University of Tampere, Finland | 0.2570 | 01 Apr 2016 | |
| 21 | BioMediTech, University of Tampere, Finland | 0.2519 | 01 Apr 2016 | |
| 22 | DeepCare Inc, China | 0.2430 | 05 Nov 2016 | |
| 23 | United Institute of Informatics Problems, Belarus | 0.2267 | 01 Apr 2016 | |
| 24 | Smart Imaging Technologies, USA | 0.2081 | 01 Apr 2016 | |
| 25 | HTW-BERLIN, Germany | 0.1867 | 01 Apr 2016 | |
| 26 | Technical University of Munich (CAMP), Germany | 0.1837 | 01 Apr 2016 | |
| 27 | University of South Florida, Computer Science and Engineering, USA | 0.1793 | 01 Apr 2016 | |
| 28 | NSS college of Engineering, India | 0.1652 | 01 Apr 2016 | |
| 29 | VISILAB, University of Castilla-La Mancha, Spain | 0.1422 | 01 Apr 2016 | |
| 30 | Sorbonne Universites, Laboratoire d’Imagerie Biomdicale, France | 0.1200 | 01 Apr 2016 | |
| 31 | VISILAB, University of Castilla-La Mancha, Spain | 0.1163 | 01 Apr 2016 | |
| 32 | Mines Paris Tec, France | 0.0970 | 01 Apr 2016 | |