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.
This dataset has positive samples and negative samples. Training positive samples contain 77 images from 20 WSIs, with cell bounding boxes written in xml. Training negative samples contain 378 images from 79 WSIs.These negative WSIs have no signet ring, but could contain other kinds of tumor cells.
Each signet ring cell is labeled by experienced pathologists with a rectangle bounding box tightly surrounding the cell. Each image is of size 2000X2000. The training images are from 2 organs, including gastric mucosa and intestine. Because of the difficulty of manual annotation, there exist some signet ring cells who are missed by pathologists. In other words, this dataset is a noisy dataset with its positive images not fully annotated.
For method evaluation, another 56 patients' 227 images are utilized, in which 27 images from 11 patients contain ring cells. 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.
