Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study

Table 2 Details of the EDD2020 dataset

Aspect	Details
Source hospitals	Ambroise Paré Hospital, France; Centro Riferimento Oncologico IRCCS, Italy; Istituto Oncologico Veneto, Italy; John Radcliffe Hospital, United Kingdom
Total images	386 still images with 502 segmentation masks
Disease classes	Barrett’s esophagus: 160 masks; suspicious precancerous lesions: 88 masks; high-grade dysplasia: 74 masks; cancer: 53 masks; polyps: 127 masks
Annotation process	Performed by two clinical experts and two post-doctoral researchers using the open-source VGG image annotator annotation tool
Pre-processing pipeline	Stratified split into training (81%), validation (9%), and test (10%) using scikit-learn[34]; specialized multi-class segmentation mask generation with six channels (five disease classes + background): Non-lesion/background areas in disease images are assigned to the “normal” channel, while lesions are assigned to their respective disease channels

VGG: Visual geometry group.