Authors
Lijuan Ren, Hang Chen, Jun Yang, Lei He, Dan Tang
Published in
Biomimetics (Basel, Switzerland). Volume 11. Issue 10. Oct 14, 2026. Epub Oct 14, 2026.
Abstract
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.
PMID:
42930291
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.
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