HT-Net: A Task-Specific Dual-Branch Network for Tumor Segmentation in Histopathological Images

Breast cancer, the most prevalent malignancy among women globally, requires precise assessment of lymph node metastases in whole-slide images (WSIs) for accurate clinical staging and therapeutic planning. However, manual assessment is laborious and prone to inter-observer variability. Inspired by biomimetic principles of functional specialization and complementary visual perception, we propose Head-Tail Network (HT-Net), a novel dual-branch encoder network for histopathological image segmentation. HT-Net employs a HeadBranch with Squeeze-and-Excitation (SE) attention to prioritize background feature selection and a TailBranch with a novel Hybrid Multi-Scale Attention (HMSA) module to resolve complex tumor boundaries. Features from both branches are integrated through a spatially adaptive fusion module and reconstructed using a U-Net-style decoder. A task-specific supervision loss further enforces functional specialization between branches and mitigates class imbalance. Experiments demonstrate that HT-Net outperforms U-Net, UNet++, UCTransNet, VM-UNet, and nnWNet. On Camelyon16, HT-Net achieved 89.37% mDice and 82.05% mIoU, surpassing nnWNet by 0.86 and 1.08 percentage points, respectively. On Glas, it achieved 93.04% mDice and 86.95% mIoU, exceeding nnWNet by 0.30 and 0.47 percentage points. These results demonstrate the effectiveness of the biomimetic-inspired functional specialization strategy for automated histopathological image segmentation.