## 1 Introduction  
Deep neural networks (DNNs) have revolutionized the field of medical image analysis by learning to extract high-level abstract features in a data-driven manner, rather than the conventional hand-crafted ones with limited representative ability. Both the convolutional-based [1] and Transformer-based [2] networks have achieved enormous breakthroughs in multiple medical image analysis tasks, such as disease classification, target volume segmentation, lesion detection, and image reconstruction. Based on DNNs, impressive results on numerous diseases have been reported, including the retinopathy of prematurity (ROP) [3], retinal diseases [4, 5], breast cancer [6], lung diseases [7], and stomach diseases [8]. These encouraging results demonstrate that DNNs are promising methods to help design computer-aided diagnosis (CAD) systems. Despite the progress achieved by leveraging the DNN model, the interpretability of the outputted result is a critical aspect that clinicians interested in. A CAD system that utilizes DNNs may output the classification result (e.g., benign or malignant) based on the patient's medical imaging. However, which part of the input image associates with the output helps explain the classification result. The interpretability may also contribute to reducing the false positive or false negative samples, preventing the DNNs from failing silently when the inputs belong to the type of out-of-distribution samples [9; 10]. It has been shown that the DNNs behave as object detectors, even without the supervision of the location of the object [11]. There exist several well-known visual interpretation methods that attempt to bridge the object within the input image and the output of DNNs, e.g., guided backpropagation [12], class activation mapping (CAM) [13], and gradient-weighted class activation mapping (Grad-CAM) [14]. Based on these interpretation methods, recent works show that the DNNs indeed localize the potential lesions in recognition of multiple diseases [3; 15]. Figure 1 shows three OCT [4] samples that are diagnosed with CNV, accompanied by the visualization result of CAM. By observing the example in the first row, it can be found most of the lesions are located in the center of the image, and the result of CAM shows that the network accurately locates those abnormalities. Given the masked CAM shown on the right side of the first row, it is hard to determine the diagnosis since most lesions are masked. For the second and third examples, the lesions are scattered in the image, much more complicated than those in the first sample. Moreover, the corresponding CAM results indicate that the model only identified part of the lesions on the image's right side. In the third column, green squares are used to point out the lesions ignored by the model, and the CAM-localized regions are masked. These visualization results reveal that the DNNs may bias toward the most distinguishing features in the input, ignoring other sub-clinical lesions that contain valuable information. We suspect that the above limitation may constrain the DNNs' robustness to the variations of the lesions, causing false-negative predictions when the lesions in the image are not prominent. Ideally, it is preferred for the DNNs to recognize both the principal and subtle lesions in the input as clinicians do. Faced with the aforementioned limitation, the core idea of the proposed method is to leverage the information contained in the CAM as a guide to discover the potential ignored lesions in the input. The visual interpretation result reveals the areas the DNNs focused on. Therefore, the rest may contains ignored sub-clinical lesions that we are interested in. To discover those lesions, a vanilla approach is to fully mask the areas indicated by the visual interpretation, enforcing the DNNs to give the prediction by utilizing the rest of the regions. This approach sounds reasonable for the second and third rows shown in Figure 1, where the DNNs are possible to predict correctly based on the lesions marked by the green squares.