Brain tumor segmentation remains a significant challenge, particularly in the context of multi-modal magnetic resonance imaging (MRI) where missing modality images are common in clinical settings, leading to reduced segmentation accuracy. To address this issue, we propose a novel strategy, which is called masked predicted pre-training, enabling robust feature learning from incomplete modality data. Additionally, in the fine-tuning phase, we utilize a knowledge distillation technique to align features between complete and missing modality data, simultaneously enhancing model robustness. Notably, we leverage the Holder pseudo-divergence instead of the KLD for distillation loss, offering improve mathematical interpretability and properties. Extensive experiments on the BRATS2018 and BRATS2020 datasets demonstrate significant performance enhancements compared to existing state-of-the-art methods.

通过使用masked predicted pre-training和knowledge distillation技术，以及采用Holder pseudo-divergence代替KLD作为distillation loss，该研究在多模式磁共振成像中对脑肿瘤进行分割，取得了显著的性能提升。

揭示不完整模态脑肿瘤分割：利用掩蔽预测自编码器和发散学习