Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound

doi:10.4251/wjgo.119889

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World Journal of Gastrointestinal Oncology

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1948-5204

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

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Sep 15, 2026; 18(9): 119889
Published online Sep 15, 2026. doi: 10.4251/wjgo.119889

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Figure 4 Architecture of the MCEUS-C2Net model. CSA: Channel self-attention; DiNA: Dilated neighborhood attention; E-MHSA: Efficient multi-head self-attention; GFE: Global feature extraction; KAN: Kolmogorov-Arnold network; LFE: Local feature extraction; MHCA: Multi-head convolutional attention; LFFN: Locally feed-forward network.

Citation: Yu XY, Ye JQ, He Z, He Q. Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound. World J Gastrointest Oncol 2026; 18(9): 119889
URL: https://www.wjgnet.com/1948-5204/full/v18/i9/119889.htm
DOI: https://dx.doi.org/10.4251/wjgo.119889

Yu XY, Ye JQ, He Z, He Q. Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound. World J Gastrointest Oncol 2026; 18(9): 119889 [DOI: 10.4251/wjgo.119889]

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