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Babak Ehteshami
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We are challenging strong groups in the Machine Learning and Image Analysis field to take on the difficult task of detecting metastases in microscopic images of lymph node tissue sections. Presence of metastasis in lymph nodes of cancer patients is a poor prognostic sign and prompts for more intense treatment. Automated detection of metastases is highly meaningful and holds a great promise to improve the diagnostic process.
We are providing two large datasets (400 whole slide microscopic images of size 218,000 * 95,000 pixels) from the Radboud University Medical Center (Nijmegen, the Netherlands), as well as the University Medical Center Utrecht (Utrecht, the Netherlands). This will be the first challenge using whole-slide images in histopathology giving the participants huge amounts of data to train their systems. Please check out our website for further information about this challenge.

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

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.

Link to challenge website:
https://camelyon17.grand-challenge.org
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