CAMELYON17 Grand Challenge – Help improve diagnosis of breast cancer metastases with AI

Here is a challenge to contribute to the world health, organised by Camelyon17 and IEEE. Come forward to build a healthy world. Submission deadline is April 1, 2017s.

By Babak Ehteshami.

We are challenging strong groups in Artificial Intelligence to take on the difficult task of detecting metastases in microscopic images of lymph node tissue sections.

Camelyon17 challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI-2017)

Submission Deadline: April 1, 2017 | Challenge Event: April 18, 2017 | Location: Melbourne, Australia


Built on the success of its predecessor, CAMELYON17 is the second grand challenge in pathology organized by the Diagnostic Image Analysis Group (DIAG) and Department of Pathology of the Radboud University Medical Center (Radboudumc) in Nijmegen, the Netherlands.

The goal of this challenge is to evaluate new and existing algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists. The presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Therefore, an automated solution would hold great promise to reduce the workload of pathologists while at the same time reduce the subjectivity in diagnosis.

Last year at ISBI, we organized the highly successful CAMELYON16 grand challenge, in which 32 submissions from as many as 23 research groups were received. This was the first challenge ever using whole-slide images, having participants download over 600GB of data. This year, CAMELYON17 will invigorate the challenge by moving from slide level analysis to patient level analysis (i.e. combining the assessment of multiple lymph node slides into one outcome). This will bring the efforts closer to direct usefulness in a clinical setting. Compared to last year, the dataset is significantly extended and contains images from five medical centers.

Please check out our website for further information about this challenge:

Bio: Babak Ehteshami Bejnordi is a PhD student at the Diagnostic Image Analysis Group in RadboudUMC. He has MS in Biomedical Engineering from Chalmers University of Technology in Sweden. His Master thesis project is part of an automated cytology project involving collaboration with Uppsala University.