[2511.14599] CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.14599 (cs) [Submitted on 18 Nov 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities Authors:Dongqing Xie, Yonghuang Wu, Zisheng Ai, Jun Min, Zhencun Jiang, Shaojin Geng, Lei Wang View a PDF of the paper titled CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities, by Dongqing Xie and 6 other authors View PDF HTML (experimental) Abstract:The accurate segmentation of brain tumors from multi-modal MRI is critical for clinical diagnosis and treatment planning. While integrating complementary information from various MRI sequences is a common practice, the frequent absence of one or more modalities in real-world clinical settings poses a significant challenge, severely compromising the performance and generalizability of deep learning-based segmentation models. To address this challenge, we propose a novel Cross-Modal Compositional Self-Distillation (CCSD) framework that can flexibly handle arbitrary combinations of input modalities. CCSD adopts a shared-specific encoder-decoder architecture and incorporates two self-distillation strategies: (i) a hierarchical modality self-distillation mechanism that transfers knowledge across modality hierarchies to reduce semantic discrepancies, and (ii) a progressive modality combination distillation...