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

Figure 6 Examples of segmentation output visualizations of the top-two- and worst-performing models of each model category on the self-collected dataset. Light blue: Fully overlapping regions; Medium blue: Two-map overlaps; Dark blue: Single-map regions; Yellow: Ground truth lesions; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; EMCAD: Efficient multi-scale convolutional decoding; N: Normal; EN: Esophageal neoplasm; EV: Esophageal varices; GERD: Gastroesophageal reflux disease; GN: Gastric neoplasm; GP: Gastric polyp; GU: Gastric ulcer; GV: Gastric varices; DU: Duodenal ulcer.