Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic imagesYuki Nakajima, Kazutomo Togashi, Yuichi Sagara et al.|Endoscopy International Open|2020Cited by 26
Tu2013 IDENTIFICATION OF DEEPLY INVASIVE COLORECTAL CANCER ON NON-MAGNIFIED ENDOSCOPIC IMAGES USING ARTIFICIAL INTELLIGENCEXin Zhu, Kazutomo Togashi, Daiki Nemoto et al.|Gastrointestinal Endoscopy|2019Cited by 2
ID: 3523068 COMPUTER-AIDED DIAGNOSIS OF COLORECTAL CANCER WITH DEEP SUBMUCOSAL INVASION USING NON-MAGNIFIED WHITE LIGHT ENDOSCOPIC IMAGES COMPARED WITH ENDOSCOPISTSTakahito Takezawa, Kazutomo Togashi, Zhe Guo et al.|Gastrointestinal Endoscopy|2021Cited by 1
Differences in regions of interest to identify deeply invasive colorectal cancers: Computer-aided diagnosis vs expert endoscopistsYuki Nakajima, Kazutomo Togashi, Daiki Nemoto et al.|Endoscopy International Open|2024Cited by 1
Sa2046 WHICH REGION DOES ARTIFICIAL INTELLIGENCE LOOK AT TO PREDICT T1B COLORECTAL CANCER?: ANALYSIS BASED ON CLASS ACTIVATION MAPPING.Yuki Nakajima, Kazutomo Togashi, Daiki Nemoto et al.|Gastrointestinal Endoscopy|2020Cited by 0