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. MSB-UNet is formulated as a binary segmentation framework (tumor vs. outside region of interest), producing a two-channel probability map via a channel-wise Softmax output. 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 better results compared to other baseline methods and has the potential to enhance automated diagnostic tools for breast cancer histopathology.