MSB-UNet: A Multi-Scale Bifurcation U-Net Architecture for Precise Segmentation of Breast Cancer in Histopathology Images

Accurate segmentation of breast cancer regions in histopathological images is critical for advancing computer-aided diagnostic systems, yet challenges persist due to heterogeneous tissue structures, staining variations, and the need to capture features across multiple scales. This study introduces MSB-UNet, a novel Multi-Scale Bifurcated U-Net architecture designed to address these challenges through a dual-pathway encoder-decoder framework that processes images at multiple resolutions simultaneously. By integrating a bifurcated encoder with a Feature Fusion Module, MSB-UNet effectively captures fine-grained cellular details and broader tissue-level patterns. Evaluated on a publicly available breast cancer histopathology dataset, MSB-UNet achieves a Dice Similarity Coefficient (DSC) of 91.3% and a mean Intersection over Union (mIoU) of 84.4%, outperforming state-of-the-art segmentation models. The architecture demonstrates superior boundary precision and reduced false positives, particularly in complex cases involving infiltrative growth patterns and intra-tumoral heterogeneity. These results highlight MSB-UNet’s potential to enhance automated diagnostic tools for breast cancer histopathology, paving the way for improved clinical decision-making and future extensions to multi-class segmentation tasks.