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

doi:10.3748/wjg.v31.i41.111184

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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 3 Details of the self-collected dataset. A: Number of images in each class; B: Data distribution of each class. GERD: Gastroesophageal reflux disease.

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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