# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
###### Abstract  
Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces a novel transformer-based explainable model for breast cancer lesion segmentation (TEBLS), aimed at enhancing the accuracy and interpretability of breast cancer lesion segmentation in medical imaging. TEBLS integrates a multi-scale information fusion approach with a hierarchical vision transformer, capturing both local and global features by leveraging the self-attention mechanism. This model addresses the limitations of existing segmentation methods, such as the inability to effectively capture long-range dependencies and fine-grained semantic information. Additionally, TEBLS incorporates visualization techniques to provide insights into the segmentation process, enhancing the model's interpretability for clinical use. Experiments demonstrate that TEBLS outperforms traditional and existing deep learning-based methods in segmenting complex breast cancer lesions with variations in size, shape, and texture, achieving a mean DSC of 81.86% and a mean AUC of 97.72% on the CBIS-DDSM test set. Our model not only improves segmentation accuracy but also offers a more explainable framework, which has the potential to be used in clinical settings. breast cancer lesion segmentation; transformer; explainable model +
Footnote †: journal: Medicalidentifying potential abnormal regions, and providing more precise diagnostic information. However, traditional breast cancer detection methods are semi-automatic and rely heavily on manual intervention by radiologists, resulting in segmentation outputs with high interpretability but limited generalization capability. To achieve automated segmentation of lesion areas, CNN-based image semantic segmentation models are commonly applied in this field, typically using a local receptive field to capture image features [12; 13; 14; 15]. Although these methods can extract local features to some extent, they lack the ability to capture global semantic information. Additionally, the fixed sizes of convolutional kernels hinder CNN-based models from effectively interpreting complex semantic structures in images, restricting their segmentation accuracy in clinical settings. The rigid network architectures and parameter configurations of these models further impede their ability to model image semantics across different scales and complexities, diminishing their capacity to capture fine details in diverse and intricate medical images. In medical imaging, breast cancer lesions frequently present considerable variations in size, shape, and texture, which create challenges for CNN-based segmentation algorithms in effectively capturing both fine details and global semantic information. To address these issues, recent studies have explored transformer-based image segmentation algorithms for lesion segmentation [16; 17]. These models use convolutional layers to extract local features while leveraging self-attention to capture long-range feature correlations across regions, achieving multi-scale feature fusion along the spatial dimension. However, these models often overlook global context priors within multi-scale semantics and the fine-grained semantic information within channel dimensions. While deep learning-based image segmentation models exhibit strong feature-handling capabilities and potential applications in the medical field, their internal mechanisms remain unclear, which may lead to risks for clinical use. Understanding and explaining these models is essential to clarify their behaviours, limitations, and broader societal impacts [18]. Previous work [19] used a taxonomy of interpretability techniques and provided a structured summary based on transformer language model approaches. However, given the end-to-end nature and high nonlinear complexity of these models, further investigation is necessary to validate the interpretability of breast cancer image segmentation models utilizing imaging data.