## 1 Introduction  
Breast cancer is a prevalent and life-threatening disease that affects millions of women worldwide. Early detection of breast cancer is crucial, as it can significantly increase the chances of successful treatment and survival rates. Breast tumor segmentation, the process of identifying and delineating breast tumors from medical images, has become a crucial step in computer-aided diagnosis systems for early detection and accurate diagnosis of breast cancer [1; 2]. In recent years, several automatic breast tumor segmentation methods have been developed, ranging from traditional image processing techniques to deep learning-based approaches. These methods have the potential to assist radiologists and clinicians in improving the efficiency and accuracy of breast cancer diagnosis [3; 4]. However, despite the significant progress made in the field of automatic breast tumor segmentation, several challenges still exist. The first challenge is the inherent variability in the appearance of breast tumors, including their shape, size, texture, and contrast. Additionally, the presence of noise, artifacts, and other anatomical structures such as blood vessels and glandular tissues can complicate the segmentation process. Finally, the lack of annotated medical images for training and evaluation purposes poses a significant challenge in developing accurate and robust segmentation models [5; 6]. Machine learning and deep learning are two popular techniques used in the field of automatic breast tumor segmentation. These methods have the advantage of being able to learn from large amounts of data and automatically extract features that are relevant for tumor segmentation. Traditional machine learning models, such as support vector machines (SVMs) and random forests, have been used for breast tumor segmentation. These models rely on manually crafted features such as intensity, texture, and shape, which are then used to train the model to differentiate between tumor and non-tumor regions in medical images. However, these models can be limited by their ability to capture complex and subtle features that may be important for accurate tumor segmentation [7; 8; 9; 10]. In contrast, deep learning models, such as convolutional neural networks (CNNs), have shown promising results for automatic breast tumor segmentation. These models can learn complex and hierarchical features directly from the input images, without the need for manual feature engineering. CNNs have been shown to outperform traditional machine learning models in several studies, achieving high segmentation accuracy even in the presence of noise and other anatomical structures [11; 12]. Some of the popular deep learning models used for breast tumor segmentation include U-Net, Mask R-CNN, and FCN (Fully Convolutional Network). U-Net, in particular, has gained widespread popularity for its ability to perform accurate and efficient segmentation of medical images, including breast tumors [13; 14]. However, despite the promising results of deep learning models, they can be limited by the availability and quality of annotated medical images for training and evaluation. Additionally, the interpretability of deep learning models remains a challenge, as it can be difficult to understand how the model arrived at its segmentation decision. Therefore, there is ongoing research to develop more interpretable deep learning models for breast tumor segmentation, which can provide clinicians with greater confidence in the accuracy of the segmentation results [11; 14; 15]. [PERSON] et al. [16] presented a novel approach for breast tumor segmentation, which involves utilizing deep-feature embedded level set groups to extract semantically enriched features. The method involves training a U-Net-based network to extract different features at various stages, each of which has a unique feature depiction. At the end of each stage, a novel level-set method is integrated to produce more accurate and precise feature maps.