The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems ...
BACKGROUND: Machine learning has led to several endoscopic studies about the automated localization of digestive lesions and prediction of cancer invasion depth. Training and validation dataset collection are required for a disease in each digestive ...
Journal of gastroenterology and hepatology
Jul 28, 2020
BACKGROUND AND AIM: Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. R...
BACKGROUND: Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinati...
Journal of gastroenterology and hepatology
Jun 27, 2020
BACKGROUND AND AIM: Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There ...
The journal of pathology. Clinical research
Jun 27, 2020
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...