The CAMELYON16 challenge has ended in November 2016
PLEASE CHECK OUT CAMELYON17:
https://camelyon17.grand-challenge.org
Background
Digital pathology is a new, rapidly expanding field of medical imaging. In digital pathology, whole-slide scanners are used to digitize glass slides containing tissue specimens at high resolution (up to 160nm per pixel). The availability of digital images has garnered the interest of the medical image analysis community, resulting in increasing numbers of publications on histopathologic image analysis.
Motivation

The solution: A challenge to improve the detection of cancer metastasis
Automated detection of lymph node metastasis has a great potential to help the pathologist and reduce their workload. Within the past few years, the field has been moving towards grand goals with strong potential diagnostic impact: (fully) automated analysis of whole-slide images to detect or grade cancer, to predict prognosis or identify metastases. As such, we feel now is the right time to offer a platform for interested groups to compare strategies and algorithms for this highly meaningful task in histopathology. This will be the first challenge using whole-slide images in histopathology. The goal of the CAMELYON Challenge is to apply an open science approach to develop algorithms to detect cancer metastasis in lymph node images. Such an approach could improve the diagnosis of the patients and significantly reduce the workload of the pathologists.