## 1 Introduction  
Breast cancer is the second leading cause of cancer death in women worldwide [1, 2]. Many studies have revealed that breast screening can discover cancer in its early stages, reducing mortality [3, 4, 5, 6, 7]. Mammography has been the most widely used examination for breast screening, but it often misses cancer hidden in _dense_ breast tissue that contains much fibrous or glandular tissue rather than fat [8]. Hand-held ultrasound (HHUS) imaging is a popular alternative covering dense breast tissue, but it suffers from low reproducibility depending on its operator. Recently, automated breast ultrasound (ABUS) has been introduced and is receiving favorable reviews [9]. While an HHUS makes partial two-dimensional breast scans that need to be simultaneously examined by an operator on site, an ABUS produces whole three-dimensional (3-D) breast scans with a dedicated probe that can be asynchronously examined later. This has led to the active development of computer-aided diagnosis (CAD) systems for ABUS, which can assist interpreting physicians by reducing their workload and enhancing cancer detection performance [10, 11, 12]. The main task of CAD systems is to detect breast masses that can possibly grow into cancer. Breast masses on ABUS scans are usually shown as hypoechoic areas that can also appear due to a variety of causes, such as fat, shadow, and anechoic tissue; thus, many CAD systems erroneously detect these areas as suspicious breast masses, resulting in _false positives_. Even radiology professionals have difficulty distinguishing hypoechoic areas of breast masses from those of other causes and have been found to detect a number of false positives on breast ultrasound scans [13; 14]. Several studies [15; 16; 17; 18] have proposed image classifiers based on conventional image processing techniques but have not been reported to significantly reduce false positives. To develop competitive classifiers on ABUS, recent studies have employed convolutional neural networks (CNNs) that have shown tremendous success in image classification tasks. [PERSON] et al. [19] designed a two-stage breast mass detector on ABUS scans with a modified CNN architecture. A fast sliding window first searches for volumes of interest, which is then fed into a simple three-dimensional extension of a 2-D CNN to compute probabilities of tumor candidates. [PERSON] et el. [20] incorporated focal loss and ensemble learning into their 3-D CNN model to resolve a data imbalance issue. Although those methods have been shown to sensitively detect breast masses, they yield an average of more than four false positives per scan, which means room for improvements still remains. Simple modifications of the existing CNNs may not be sufficient to analyze the three-dimensional context of breast masses on ABUS scans. Training a CNN requires many sample images that preferably contain clear features of target objects that can be distinguished from their backgrounds. Compared to ordinary objects (e.g., cats, boats) shown in common training samples, breast masses shown in ABUS scans may not present clear boundaries. As seen in the first row of Figure 1, the breast masses (indicated by the arrows) often appear as irregularly shaded blobs, and each blob itself is hardly distinguishable from its background shadings. Moreover, the inherent low image resolution and high noise level of ABUS scans make it more difficult to train CNNs for indistinct masses. Considering these characteristics, we empirically found that training with a segmentation _mask_ on a mass helps improve the classification performance, probably because the low-level features on the blobs are activated on the masses rather than on the backgrounds. The second row of Figure 1 shows class activation maps (CAMs) for mass classification using the existing DenseNet [21]. The activated network weights are widely distributed across the background blobs and are not concentrated on the masses.