Background. Accurate pathological diagnosis of gastric endoscopic biopsy could greatly improve the opportunity of early diagnosis and treatment of gastric cancer. The Japanese “Group classification” of gastric biopsy corresponds well with the endoscopic diagnostic system and can guide clinical treatment. However, severe shortage of pathologists and their heavy workload limit the diagnostic accuracy. This study presents the first attempt to investigate the applicability and effectiveness of AI-aided system for automated Japanese “Group classification” of gastric endoscopic biopsy. Methods. In total, 260 whole-slide images of gastric endoscopic biopsy were collected from Dalian Municipal Central Hospital from January 2015 to January 2021. These images were annotated by experienced pathologists according to the Japanese “Group classification.” Five popular convolutional neural networks, i.e., VGG16, VGG19, ResNet50, Xception, and InceptionV3 were trained and tested. The performance of the models was compared in terms of widely used metrics, namely, AUC (area under the receiver operating characteristic curve, i.e., ROC curve), accuracy, recall, precision, and F1 score. Results. Results showed that ResNet50 achieved the best performance with accuracy 93.16% and AUC 0.994. Conclusion. Our results demonstrated the applicability and effectiveness of DL-based system for automated Japanese “Group classification” of gastric endoscopic biopsy.

中文翻译：

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日本胃内镜活检全幻灯片图像“组分类”深度学习模型的初步研究

背景。胃内镜活检的准确病理诊断可以大大提高胃癌的早期诊断和治疗机会。日本的胃活检“组分类”与内镜诊断系统吻合较好，可以指导临床治疗。然而，病理学家的严重短缺和繁重的工作量限制了诊断的准确性。本研究首次尝试研究人工智能辅助系统在日本胃内镜活检自动“组分类”中的适用性和有效性。方法. 从 2015 年 1 月至 2021 年 1 月，从大连市中心医院共收集了 260 张胃内镜活检的全幻灯片图像。这些图像由经验丰富的病理学家根据日本“组分类”进行注释。训练和测试了五种流行的卷积神经网络，即 VGG16、VGG19、ResNet50、Xception 和 InceptionV3。在广泛使用的指标方面比较模型的性能，即AUC（接收器操作特征曲线下的面积，即ROC曲线）、准确度、召回率、精确度和F1分数。结果。结果表明，ResNet50 以 93.16% 的准确率和 0.994 的 AUC 实现了最佳性能。结论. 我们的结果证明了基于 DL 的系统在自动日本胃内镜活检“组分类”中的适用性和有效性。