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Cited by 264 publications
(165 citation statements)
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References 22 publications
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“…29 Using a larger dataset of 790 WLE images and 203 for testing, DL revealed limited sensitivity (76%) but was highly specific (96%) for SM2 or deeper invasive cancers-a useful scenario where specificity is the priority for the decision of resection strategy. 30 Gastric mapping using image retrieval networks by extracting descriptors from an index endoscopic examination provides the potential for real-time guidance for retargeting areas of concern which could be extended for colonic surveillance. 31…”
Section: Gastric Cancermentioning
confidence: 99%
“…29 Using a larger dataset of 790 WLE images and 203 for testing, DL revealed limited sensitivity (76%) but was highly specific (96%) for SM2 or deeper invasive cancers-a useful scenario where specificity is the priority for the decision of resection strategy. 30 Gastric mapping using image retrieval networks by extracting descriptors from an index endoscopic examination provides the potential for real-time guidance for retargeting areas of concern which could be extended for colonic surveillance. 31…”
Section: Gastric Cancermentioning
confidence: 99%
“…Zhu et al . advanced this research area by developing the notable DL algorithm that enabled identification of SM2 from M/SM1.…”
Section: Stomachmentioning
confidence: 99%
“…Zhu et al 19 advanced this research area by developing the notable DL algorithm that enabled identification of SM2 from M/SM1. A total of 790 conventional endoscopic images of gastric cancers were used for machine learning, while an additional 203 images, which were completely independent from the learning material, were used as a test set.…”
Section: Identification Of Gastric Cancermentioning
confidence: 99%
“…CADx has been used in the upper GI tract for the diagnosis of esophageal cancer, Helicobacter pylori ( H. pylori ) infection and early gastric cancer . Horie et al described a CNN‐based model for identifying esophageal cancer which could distinguish superficial esophageal cancer from advanced cancer with an accuracy of 98%.…”
Section: Current Situation Of Ai‐aided Endoscopic Image Recognitionmentioning
confidence: 99%
“…Kanesaka et al studied a CADx system to identify and delineate early gastric cancer in real‐time magnifying NBI, with an accuracy of 96.3%. Zhu et al proposed a CNN CAD system for predicting the invasion depth of gastric cancer (M/SM1 vs deeper than SM1). Its accuracy (89.16%) and specificity (95.56%) were significantly better than those of experienced endoscopists.…”
Section: Current Situation Of Ai‐aided Endoscopic Image Recognitionmentioning
confidence: 99%