## 1 Introduction
However, it is hard to predict the first sample based on the masked input in Figure 1, since critical information in the input is not given. A desirable approach is to moderately mask the potential lesions with the help of visual interpretations without the complete loss of valuable information. Thus, the DNNs can adapt to the inputs that contain either simplified or complicated lesions. Based on this consideration, this paper proposes a simple yet effective data augmentation method called guided random mask (GRM), which randomly masks the areas indicated by the visual interpretation during the training phase. The term \"guided\" in GRM refers to the information provided by the visual interpretation, and \"random mask\" implies the stochasticity that produces the effect of regularization. The proposed GRM is a data augmentation method that can prevent the DNNs from focusing only on prominent input lesions and can better utilize spatial contextual information. Notably, the GRM is a parameter-free method that can accommodate lesions with different scales and complexity. It is known that data augmentation plays a vitally important role in the training of DNNs to mitigate the problem of over-fitting. One inspiration of the proposed GRM is the cutout [16], which randomly masks the input with a fixed size area and helps the DNNs achieve state-of-the-art performance on CIFAR [17] and SVHN [18] datasets. However, compared with the CIFAR and SVHN with relatively small image sizes (32 \(\times\) 32), it is much harder to apply the cutout to high-resolution medical images because of the hyper-parameter tuning. The hyper-parameter in the cutout is the size of the mask, which can be regarded as the strength of regularization in training DNNs. Its optimal value is task-dependent and requires grid search to achieve the best performance, which may limit its effectiveness in practice. On the contrary, the proposed GRM eliminates the difficulty in hyper-parameter tuning by using the guidance provided by the visual interpretation. Unlike the fixed mask size in cutout, the one in the proposed GRM is adaptively adjusted along with the training of DNNs, making it applicable to recognition tasks with varied scales and complexity of lesions. The contributions of the paper can be summarized as follows:  
1. We found that the DNNs may bias toward the most prominent features and ignore the sub-clinical ones when the input image contains complicated lesions. 2. A parameter-free data augmentation method called GRM is proposed, which utilizes visual interpretation of the prediction result to regularize the training of DNNs adaptively. 3. Visual interpretation demonstrates that DNNs coupled with GRM can more effectively utilize the contextual information than the vanilla models. Figure 1: Three choroidal neovascularization (CNV) samples containing a neovascular membrane. Each row represents an optical coherence tomography (OCT) sample. The three columns denote the original OCT image, the visualization result of CAM, and the image with CAM masked, respectively. For the right-most image in all three rows, the white masks represent the result area of CAM, and the green squares denote the lesions ignored by DNNs. Detailed illustration of dataset and computation principle of CAM can be found in Section 3. * Ablation studies on multiple datasets, including OCT, X-ray, and ultrasound images, empirically show that the GRM substantially surpasses the benchmark method on various tasks. The rest of the paper is organized as follows. Section 2 summarizes the related works about the applications in medical imaging and common augmentation methods used for the training of DNNs. Section 3 first illustrates the three types of medical imaging datasets used, followed by a detailed explanation of the proposed method. Section 4 shows the results of extensive experiments, including the comparison of the baseline model and other well-known related augmentation methods. Visualization analysis is also used to verify the effectiveness of the GRM.