Endoscopic detection and differentiation of esophageal lesions using a deep neural network

Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentiate esophageal SCC. A total of 9591 nonmagnified endoscopy (non-ME) and...

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Veröffentlicht in:	Gastrointestinal endoscopy Jg. 91; H. 2; S. 301 - 309.e1
Hauptverfasser:	Ohmori, Masayasu, Ishihara, Ryu, Aoyama, Kazuharu, Nakagawa, Kentaro, Iwagami, Hiroyoshi, Matsuura, Noriko, Shichijo, Satoki, Yamamoto, Katsumi, Nagaike, Koji, Nakahara, Masanori, Inoue, Takuya, Aoi, Kenji, Okada, Hiroyuki, Tada, Tomohiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.02.2020
Schlagworte:	Adult Aged Aged, 80 and over Deep Learning Esophageal Diseases - diagnostic imaging Esophageal Diseases - pathology Esophageal Neoplasms - diagnostic imaging Esophageal Neoplasms - pathology Esophageal Squamous Cell Carcinoma - diagnostic imaging Esophageal Squamous Cell Carcinoma - pathology Esophagus - pathology Female Humans Image Processing, Computer-Assisted - methods Male Middle Aged Narrow Band Imaging - methods Neoplasm Invasiveness Neural Networks, Computer Observer Variation Optical Imaging - methods Precancerous Conditions - diagnostic imaging Precancerous Conditions - pathology Reproducibility of Results Sensitivity and Specificity CNN SCC NBI WLI AI ME BLI
ISSN:	0016-5107, 1097-6779, 1097-6779
Online-Zugang:	Volltext
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