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

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World Journal of Gastroenterology

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1007-9327

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World J Gastroenterol. Nov 7, 2025; 31(41): 111184
Published online Nov 7, 2025. doi: 10.3748/wjg.v31.i41.111184

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Figure 5 Visualization of the specialized multi-class mask for the EDD2020 dataset. HGD: High-grade dysplasia; BE: Barrett’s esophagus.

Citation: Chan IN, Wong PK, Yan T, Hu YY, Chan CI, Qin YY, Wong CH, Chan IW, Lam IH, Wong SH, Li Z, Gao S, Yu HH, Yao L, Zhao BL, Hu Y. Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study. World J Gastroenterol 2025; 31(41): 111184
URL: https://www.wjgnet.com/1007-9327/full/v31/i41/111184.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i41.111184

Chan IN, Wong PK, Yan T, Hu YY, Chan CI, Qin YY, Wong CH, Chan IW, Lam IH, Wong SH, Li Z, Gao S, Yu HH, Yao L, Zhao BL, Hu Y. Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study. World J Gastroenterol 2025; 31(41): 111184 [PMID: 41262538 DOI: 10.3748/wjg.v31.i41.111184]

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