Distilling population specific expertise into a unified model for generalizable brain tumor segmentation

Multimodal brain tumor segmentation models often struggle to generalize across diverse populations due to variations in tumor pathology, patient demographics, and imaging protocols. A common approach to mitigate these challenges involves training separate models per population, employing ensemble methods, fine-tuning pretrained networks, or adopting curriculum learning strategies. While these approaches may yield improvements within specific domains, they often suffer from limited scalability, increased inference cost, poor adaptability to heterogeneous populations, and susceptibility to overfitting or catastrophic forgetting. To address these challenges, we propose a novel Multi-Teacher Single-Student Knowledge Distillation framework (MTSS-KDNet), built on the specialized knowledge of individual teacher models and distilling their collective expertise into a unified student model. Our framework performs population-aware knowledge transfer, guiding the student to integrate the strengths of multiple specialized teachers through both latent- and output-level supervision. This enables effective and independent generalization across all tumor types. In this paper, we focus on five distinct tumor populations: Adult Gliomas, Pediatric Gliomas, Sub-Saharan African Gliomas—which, although pathologically similar to their adult counterparts, often suffer from degraded MRI image quality—Intracranial Meningiomas and Brain Metastases. These tumor types exhibit unique developmental, morphological, anatomical and imaging characteristics, introducing heterogeneity that poses significant challenges to the ability of models to generalize accurately. Our approach achieves superior performance across all five populations, with average dice scores (DSC) of 0.87, 0.84, and 0.77 in the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) regions, respectively, outperforming both population-specific and strong benchmark models. These results highlight the robustness and versatility of our method, offering a promising solution for enhancing generalizability in brain tumor segmentation while facilitating seamless clinical deployment.