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

作者：In Neng Chan, Pak Kin Wong, Tao Yan, Yanyan Hu, Chon In Chan, Ye-Ying Qin, Chi Hong Wong, In Weng Chan, Ieng Hou Lam, Sio Hou Wong, Zheng Li, Shan Gao, Hon Ho Yu, Liang Yao, Baoliang Zhao, Ying Hu · 发表于：World Journal of Gastroenterology · 年份：2025 · DOI：10.3748/wjg.v31.i41.111184 · 被引用次数：1 · 研究领域：Colorectal Cancer Screening and Detection、AI in cancer detection、COVID-19 diagnosis using AI

BACKGROUND: Upper gastrointestinal (UGI) diseases present diagnostic challenges during endoscopy due to visual similarities, indistinct boundaries, and observer variability, which can lead to missed diagnoses and delayed treatment. Automated segmentation using deep learning (DL) models offers the potential to assist endoscopists, improve diagnostic accuracy, and reduce workload. However, multi-class UGI disease segmentation remains underexplored, with limited annotated datasets and insufficient focus on clinical validation. This study hypothesizes that comparative analysis of different DL architectures can identify models suitable for clinical application, providing actionable insights to reduce diagnostic errors and support clinical decision-making in endoscopic practice. AIM: To evaluate 17 state-of-the-art DL models for multi-class UGI disease segmentation, emphasizing clinical translation and real-world applicability. METHODS: This study evaluated 17 DL models spanning convolutional neural network (CNN)-, transformer-, and mamba-based architectures using a self-collected dataset from two hospitals in Macao and Xiangyang (3313 images, 9 classes) and the public EDD2020 dataset (386 images, 5 classes). Models were assessed for segmentation performance and performance-efficiency trade-off. Statistical analyses were conducted to examine performance differences across architectures. Generalization capability was measured through a cross-dataset evaluation (training models on th...