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Tada Tomohiro The Institute of Gastroenterology & Proctology
Homepage:
tada-endoscope.com
Alternate name:
ただともひろ胃腸科肛門科
ROR:
04dav1h78
OpenAlex ID:
I4405256651
Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
2018
·
Toshiaki Hirasawa
, Kazuharu Aoyama
, et al.
·
Gastric Cancer
·
776
PDF
Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks
2018
·
Yoshimasa Horie
, Toshiyuki Yoshio
, et al.
·
Gastrointestinal Endoscopy
·
476
Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
2018
·
Tomonori Aoki
, Atsuo Yamada
, et al.
·
Gastrointestinal Endoscopy
·
309
Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images
2017
·
Satoki Shichijo
, Shuhei Nomura
, et al.
·
EBioMedicine
·
305
PDF
Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis
2018
·
Tsuyoshi Ozawa
, Soichiro Ishihara
, et al.
·
Gastrointestinal Endoscopy
·
234
Detecting early gastric cancer: Comparison between the diagnostic ability of convolutional neural networks and endoscopists
2020
·
Yohei Ikenoyama
, Toshiaki Hirasawa
, et al.
·
Digestive Endoscopy
·
183
PDF
Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network
2020
·
Hiroaki Saito
, Tomonori Aoki
, et al.
·
Gastrointestinal Endoscopy
·
174
Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images
2019
·
Akiyoshi Tsuboi
, Shiro Oka
, et al.
·
Digestive Endoscopy
·
170
Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks
2020
·
Tsuyoshi Ozawa
, Soichiro Ishihara
, et al.
·
Therapeutic Advances in Gastroenterology
·
163
PDF
Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging
2020
·
Hiroya Ueyama
, Yusuke Kato
, et al.
·
Journal of Gastroenterology and Hepatology
·
162
Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists
2019
·
Kentaro Nakagawa
, Ryu Ishihara
, et al.
·
Gastrointestinal Endoscopy
·
158
Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging
2019
·
Yusuke Horiuchi
, Kazuharu Aoyama
, et al.
·
Digestive Diseases and Sciences
·
154
Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks
2018
·
Hirotoshi Takiyama
, Tsuyoshi Ozawa
, et al.
·
Scientific Reports
·
150
PDF
Endoscopic detection and differentiation of esophageal lesions using a deep neural network
2019
·
Masayasu Ohmori
, Ryu Ishihara
, et al.
·
Gastrointestinal Endoscopy
·
139
PDF
Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading
2019
·
Tomonori Aoki
, Atsuo Yamada
, et al.
·
Digestive Endoscopy
·
121
Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma
2020
·
Yoshitaka Tokai
, Toshiyuki Yoshio
, et al.
·
Esophagus
·
115
Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network
2019
·
Tomonori Aoki
, Atsuo Yamada
, et al.
·
Journal of Gastroenterology and Hepatology
·
112
Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging
2020
·
Sayaka Nagao
, Yosuke Tsuji
, et al.
·
Gastrointestinal Endoscopy
·
110
Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus
2018
·
Youichi Kumagai
, Kaiyo Takubo
, et al.
·
Esophagus
·
97
Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video)
2020
·
Hiromu Fukuda
, Ryu Ishihara
, et al.
·
Gastrointestinal Endoscopy
·
95
Application of convolutional neural networks for evaluatingHelicobacter pyloriinfection status on the basis of endoscopic images
2019
·
Satoki Shichijo
, Yuma Endo
, et al.
·
Scandinavian Journal of Gastroenterology
·
92
Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos)
2020
·
Yusuke Horiuchi
, Toshiaki Hirasawa
, et al.
·
Gastrointestinal Endoscopy
·
90
Detecting gastric cancer from video images using convolutional neural networks
2018
·
Mitsuaki Ishioka
, Toshiaki Hirasawa
, et al.
·
Digestive Endoscopy
·
86
PDF
Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study
2020
·
Tomonori Aoki
, Atsuo Yamada
, et al.
·
Gastrointestinal Endoscopy
·
76
PDF
Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
2021
·
Ryota Niikura
, Tomonori Aoki
, et al.
·
Endoscopy
·
66
PDF
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Works count:
84
Citation count:
14,980
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