Autooral dataset

A multi-tasking oral ulcer dataset (Autooral dataset) is proposed. Autooral dataset contains two major tasks of disease segmentation and classification. The labeling of the Autooral dataset was done by three experienced dentists. At the end of the annotation, we formed 420 images of oral data with high quality after cropping and removal operations. We standardize the image size to 256*256. The original image is a 24-bit RGB image, the ground truth for the segmentation task is an 8-bit image, and there are five different disease types for the classification task (including cancerous ulcers, traumatic ulcers and traumatic blood blisters, herpes-like aphthous ulcers, mild aphthous ulcers, severe aphthous ulcers). The ratio of the 5 different ulcer types for the classification task was 9:9:15:18:22 (with a few exclusions). Further, by chi-square test, there were significant differences in gender (p=0.04) and age (p=0.01) of the patients among the 5 ulcer types. All-age coverage, a 13-year collection interval, and the presence of 12 underlying diseases, among other things, demonstrated that we had sufficient sample diversity.