Background Deep learning models have previously been established to predict the
histopathology and invasion depth of gastric lesions using endoscopic images. This study …

Background and Aims We aimed to develop and validate a deep learning–based system
that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting …

Background Gastric cancer is a common kind of malignancies, with yearly occurrences
exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues …

Background Visual inspection, lesion detection, and differentiation between malignant and
benign features are key aspects of an endoscopist's role. The use of machine learning for …

This study developed a new convolutional neural network model to detect and classify
gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light …

Background We aimed to develop and validate a real-time deep convolutional neural
networks (DCNNs) system for detecting early gastric cancer (EGC). Methods All 45,240 …

Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast
Asia. Population-based endoscopic screenings in the region have yielded successful results …

BACKGROUND: A timely diagnosis of early gastric cancer (EGC) can greatly reduce the
death rate of patients. However, the manual detection of EGC is a costly and low-accuracy …

Endoscopy is widely applied in the examination of gastric cancer. However, extensive
knowledge and experience are required, owing to the need to examine the lesion while …

Endoscopic resection is recommended for gastric neoplasms confined to mucosa or
superficial submucosa. The determination of invasion depth is based on gross morphology …