摘要：Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam’s decision making. Highly-trained pathologists are needed for carefulmicroscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process . A reliable decision support system would assist healthcare systems that often sufer from a shortage of pathologists . Recent advances in digital pathology allow for high-resolution digitalization of pathological slides . Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times . In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes . The 200 digital slides, after pre- processing, resulted in 101,389 patches . A single patch is a 512 × 512 pixel image, covering 248 × 248 μm2 tissue area . Versions at higher resolution are available as well . Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research .
