The dataset consists of 400 whole-slide images (WSIs) of lymph node sections stained with hematoxylin and eosin (H&E), collected from two medical centers in the Netherlands. The WSIs are stored in a multi-resolution pyramid format, allowing for efficient retrieval of image subregions at different magnification levels. The training set includes two subsets:

• 170 WSIs (100 normal, 70 with metastases) from Radboud University Medical Center
• 100 WSIs (60 normal, 40 with metastases) from University Medical Center Utrecht

The test set consists of 130 WSIs from both institutions. Ground truth data for metastases is provided as XML files with annotated contours and WSI binary masks.

The Camelyon16 dataset aims to reduce the workload and subjectivity in cancer diagnosis by pathologists. It serves as a benchmark for evaluating algorithms that can automatically detect metastases in histopathological images, focusing on breast cancer in sentinel lymph nodes.

Researchers can develop and refine machine learning models for automated detection of metastases. The dataset allows for performance comparisons of different detection algorithms. Automated systems can be integrated into clinical workflows to enhance diagnostic accuracy and efficiency. The dataset is valuable for training medical professionals in digital pathology and AI applications in diagnostics.