## 1 Introduction  
Among all malignant tumors, the incidence and mortality of gastric cancer is in the forefront. Clinically, gastroscopy and biopsy for pathological diagnosis are still the gold standard methods for the examination of early gastric cancer. As the resources of digital gastric pathology images are being accumulated, many efforts have been made in designing computer-aided pathological image analysis systems to reduce the workload of pathologists and improve the efficiency of cancer diagnosis. Among all the pathological image analysis tasks, the most important is the classification task, which is the cornerstone and bottleneck of other in-depth studies, such as nuclei localization, gland segmentation, etc. The early pathological image classification tasks are based on conventional machine learning algorithms. Common classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), etc. were used. Due to the high resolution of pathological images, some pre-processing should be performed prior to applying machine learning algorithms. The mainstream method is firstly to divide the whole pathological slide image into small local patches. Then, feature extraction and classification between cancer and non-cancer are performed in each local patch. Finally, the prediction results of these patches are integrated through certain methods, such as majority voting, to determine the classification results of image level or case level. At the feature extraction stage, meaningful features such as gray level co-occurrence matrix (GLCM), local binary pattern (LBP), histogram of oriented gradient (HOG), scale-invariant feature transform (SIFT), or certain well-designed, handcrafted features related to cell morphology and density can be utilized for subsequent pathology image analysis tasks. Due to the high computing requirements in the process of handling high-resolution WSIs, extracting effective features to reduce data dimension is an important problem. However, handcrafted feature extraction relies heavily on professional domain knowledge and cannot guarantee self-discrimination and completeness, which restricts the application of conventional machine learning algorithms in pathological image analysis tasks. Recently, due to the remarkable performance of deep learning methods in the field of computer vision, most of the image recognition techniques have been replaced by deep learning. This is also true for pathological image analysis. When locations of abnormal or cancerous areas in WSIs are provided by pathologists, some state-of-the-art convolutional neural networks (CNNs) can be directly applied in the patch-level classification stage. [PERSON] et al. [1] proposed a well-designed CNN model for automatic classification of gastric carcinoma. [PERSON] et al. [2] proposed a two-stage framework including a fully convolutional location network for discriminative instance selection and recalibrated multi-instance deep learning (RMDL) for gastric WSI classification. Additionally, pixel-wise annotations are necessary to train a representative location network. On the other hand, extracting informative features based on CNNs pre-trained on the ImageNet has become an effective method of data pre-processing. Some recent studies suggest ImageNet-trained CNNs are strongly biased towards recognizing texture information [3]. In a whole pathological slide, clustered cells with the same structure and function exhibit a similar textured appearance. Therefore, the pathological image is rather closer to a type of order-less texture-like image [4]. Multiple typical CNNs architectures have been taken into account to demonstrate that the internal layers of CNNs can act as feature extractors and generalize well to pathological images [5; 6]. However, it is time-consuming and labor-intensive to ask experts to annotate a large number of high-resolution pathological images in detail. The scarcity of annotations has been a great challenge for the task of classifying pathological images.