Research status and progress of deep learning in automatic esophageal cancer detection

doi:10.4251/wjgo.v17.i5.104410

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May 15, 2025 (publication date) through May 15, 2025

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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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World J Gastrointest Oncol. May 15, 2025; 17(5): 104410
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.104410

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Figure 1 Common diagnostic modalities for esophageal cancer in current clinical practice. A: Endoscopy; B: Pathological sections stained with hematoxylin-eosin; C: Esophageal barium swallow; D: Plain computed tomography; E: ¹⁸F-fluorodeoxyglucose positron emission tomography; F: ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography.

Citation: Chen J, Fan X, Chen QL, Ren W, Li Q, Wang D, He J. Research status and progress of deep learning in automatic esophageal cancer detection. World J Gastrointest Oncol 2025; 17(5): 104410
URL: https://www.wjgnet.com/1948-5204/full/v17/i5/104410.htm
DOI: https://dx.doi.org/10.4251/wjgo.v17.i5.104410