HECKTOR 2025 Challenge: Hierarchical Multi-modal Vision Network for Head-and-Neck Tumor Segmentation and Survival Prediction

Beining Wu, Enyu Bao, Zhiling Li, Chen Yilei, Feiwei Qin, Yifei Chen

Keywords: Head and Neck Cancer, Tumor Segmentation, Survival Prediction, Multimodal Deep Learning, PET/CT, Vision Transformer

Abstract: Head and neck cancer poses major challenges for precision oncology, where accurate tumor segmentation and reliable survival prediction are essential yet remain difficult due to heterogeneous morphology and multi-center variability in PET/CT. We introduce HM-VNet, a unified multimodal framework designed to address these challenges through end-to-end learning. For segmentation, HM-VNet integrates hierarchical Transformer-based encoding with multimodal fusion to achieve robust delineation of both primary tumors and metastatic lymph nodes. For survival prediction, a deep cross-modal fusion network combines imaging, clinical, and radiomic features, further guided by anatomical priors derived from segmentation results. Comprehensive evaluation on the HECKTOR 2025 Challenge confirms that HM-VNet consistently outperforms state-of-the-art approaches, demonstrating strong effectiveness and promising clinical relevance in advancing automated multimodal intelligence for head and neck cancer management. Our source code is available at https://github.com/Wu-beining/HM-VNet. (Team: HDUMedAI)

Submission Number: 3