In the challenge, participants will be provided with 2 datasets: 

Signet ring cell dataset

Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection of such cells leads to huge improvement of patients' survival rate. However, there is no existing public dataset with annotations for studying the problem of signet ring cell detection. A total of 90 patients' 450 pathological images will be provided as the training set. Each signet ring cell is labeled by a rectangle bounding box tightly surrounding the cell. Each image is of size 2000X2000 and there is a total of 15,000 cells annotated by experienced pathologists, each labeled cell is indeed signet ring cell. The training images are from 2 organs, including gastric mucosa and intestine. For method evaluation, another 30 patients' 150 images and annotations of 5,000 cells will be utilized. For the normal region false positives, some negative samples which are either healthy or infected by other types of cancer will be added for training and evaluation. All whole slide images were stained by hematoxylin and eosin and scanned at X40. 

Sample of parts of images:

Sample of a full image:

Colonoscopy tissue segment dataset

Colonoscopy pathology examination can find cells of early-stage colon tumor from small tissue slices. Pathologists need to daily examine hundreds of tissue slices, which is a time consuming and exhausting work. Here we propose a challenge task on automatic colonoscopy tissue segmentation and screening, aiming at automatic lesion segmentation and classification of the whole tissue (benign vs. malignant). A total of 450 patients' 750 tissue slices of an average size of 3000x3000 will be provided as the training set. The fine pixel-level annotations of lesion and the diagnosis of the tissues will be labelled by experienced pathologists. We will also provide another 150 patients' 250 tissues as the testing set. The data in the challenge will show great variations in terms of appearance because the data are collected from multiple medical centers, especially from several small centers in developing countries/regions. Image style differences can be an obstacle for the screening task. Holding the challenge and releasing the large quantity of expert level annotations will attract much attention from the medical imaging community and substantially advance the research on automatic colonoscopy screening. All whole slide images were stained by hematoxylin and eosin and scanned at X20.

Sample of part of image:

Sample of a full image: