###### Abstract  
Breast cancer is one of the most common female diseases, posing a great threat to women's health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization. breast tumor segmentation; U-Net; residual depthwise separable convolution; multi-scale channel attention gate  
2023 1  
results for benign tumor regions with clear and well-defined boundaries, they often fall short when confronted with the challenges posed by irregular tumor regions and blurred boundaries. Similar traditional segmentation methods encompass threshold segmentation [10,11], cluster-based segmentation [12,13], watershed algorithm [14], graph-based segmentation [15,16], and more. These techniques encounter similar challenges when applied to the segmentation of complex ultrasound images of breast tumors. With the continuous advancement of machine learning methods, artificial intelligence has revolutionized traditional approaches to solving a wide range of problems across various fields, particularly in the realm of biomedicine [17]. The utilization of artificial intelligence algorithms allows for more precise and efficient solutions to challenges like protein structure prediction, gene sequence data mutation recognition, diagnosis of bone diseases [18,19], and organ lesion segmentation. The introduction of convolutional neural networks (CNNs) has further catalyzed progress in biological research. CNNs are versatile in handling diverse types of data. One-dimensional CNNs slide in a single direction and are well-suited for data with only one spatial dimension, such as text or biological sequences. In contrast, two-dimensional CNNs can process data with two spatial dimensions, such as CT images, while 3D CNNs are designed for volumetric data like magnetic resonance imaging scans. Biological data often exhibit a pronounced local structure, and the recognition of these structures or patterns is crucial for analysis. CNNs inherently excel at capturing local features and possess robust feature extraction capabilities. Consequently, the integration of interdisciplinary knowledge and methods, along with the application of CNNs, is poised to drive further breakthroughs and innovations in the field of biological research. The diagnosis and treatment of breast cancer is an important application in biomedical research. Through automatic segmentation of breast ultrasound images, doctors can evaluate disease conditions more efficiently and give diagnostic opinions. At present, more and more studies are combining CNNs to achieve segmentation of breast ultrasound images.