## 1 Introduction  
The global incidence of gastric cancer and its subsequent mortality are high [1; 2]. Furthermore, the incidence of gastric cancer increases with age and is expected to further increase with increasing life expectancy worldwide [3]. Gastric cancer is assessed using X-rays or endoscopy. Endoscopic examination is especially important for the early detection of gastric cancer because of its high sensitivity [4]. However, the number of possible daily endoscopic examinations is limited, as they must be performed by a specialist. Diagnoses can differ depending on the experience and the technique of the specialist; therefore, it is an important responsibility of the specialist. Furthermore, morphological changes in early gastric cancer are sometimes obscure; hence, there are also concerns about missing the early detection of gastric cancer. The miss rate of gastric cancer according to the endoscopic examination was 22.2%, even when two experts were involved in an endoscopic unit [5]. Similar studies have shown that gastric cancer is overlooked at a certain rate during endoscopy [6; 7; 8; 9]. Although endoscopic examination is an excellent tool for detecting gastric cancer, the aforementioned challenges remain. We believe that it is possible to overcome these challenges using computers. Computer-aided diagnosis (CAD) is an effective method in this regard, as it involves the quantification and the analyses of imaging data by a computer. The results can be used by physicians as a diagnostic support tool. This method also overcomes the differences in diagnoses attributable to the differing skills and experiences of specialists. Many CAD schemes have introduced a deep learning approach [10; 11; 12; 13; 14; 15; 16; 17; 18]. In a study involving gastric endoscopic images, [PERSON] et al. developed a patch-based classification algorithm using a convolutional neural network (CNN) to detect gastric cancer in endoscopic images [16]. The result of the evaluation showed that the accuracy of the trained CNN was 87.6%. [PERSON] et al. also developed an automated detection and depth estimation method for gastric cancer using a CNN [17]. They introduced the activation map using gradient-weighted class activation mapping to visualize the attention of the CNN. They revealed that the CNN focuses on the gastric cancer region correctly in detection. [PERSON] et al. [18] proposed an automated detection method for gastric cancer using a single-shot multi-box detector (SSD) for object detection and reported a sensitivity of 92.2% and a positive predictive value of 30.6%. The SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At the prediction stage, the network generates scores for the presence of each object category in each default box and enables adjustments to the box to better match the object shape. However, the high false-positive rate remains unaddressed. Furthermore, physicians cannot understand the invasive regions of gastric cancer from the bounding box provided by the previous method. To overcome these challenges, we focus on segmentation to correctly recognize and extract objects from images. In this regard, several common segmentation techniques have been proposed. [PERSON] et al. proposed a fully convolutional neural network (FCN) [19]. An FCN replaces the fully connected layer of a CNN with an up-sampling layer. Each pixel is labeled with the class of the enclosing object. This segmentation method is called \"semantic segmentation.\" [PERSON] et al. proposed U-Net as a semantic segmentation technology for biomedical images [20]. U-Net has an encoder layer that performs down-sampling while extracting features from an image and a decoder layer that up-samples an image. A bypass connection is provided between the encoder and the decoder layers. This structure enables the segmentation of an object without dropping the resolution of an image.