The CAMELYON16 challenge has ended in November 2016
PLEASE CHECK OUT CAMELYON17:
The goal of this challenge is to evaluate new and existing algorithms for automated detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images of lymph node sections. This task has a high clinical relevance but requires large amounts of reading time from pathologists. Therefore, a successful solution would hold great promise to reduce the workload of the pathologists while at the same time reduce the subjectivity in diagnosis. This will be the first challenge using whole-slide images in histopathology. The challenge will run for two years. The 2016 challenge will focus on sentinel lymph nodes of breast cancer patients and will provide a large dataset from both the Radboud University Medical Center (Nijmegen, the Netherlands), as well as the University Medical Center Utrecht (Utrecht, the Netherlands).
CAMELYON16 in news and media
Camelyon16 was a highly successful challenge with 32 submissions from as many as 23 teams. The results of our challenge were widely reflected in the news and reports including:
- Google Research Blog
- White House report on "The national AI research and development strategic plan"
- Nvidia blog
- Tonic on the Google results
- and many more articles.
Number of participants: 389
20th November 2016: Camelyon17 is open for registration. Registration to Camelyon16 is closed.
14th April 2016: Submission page is reopened.
13th April 2016: The challenge workshop took place.
1st April 2016: Submission deadline for results.
1st March 2016: The test dataset is released.
30 December 2015: The second training dataset is released.
25 November 2015: The first training dataset is released.
25 November 2015: Registration is opened.
15 October 2015: Challenge website launched.
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM, and the CAMELYON16 Consortium. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199–2210. doi:10.1001/jama.2017.14585