Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning
<|reference_start|>Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning: 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.<|reference_end|>
@article{sun2024unveiling,
    title={Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging
  Masked Predicted Auto-Encoder and Divergence Learning},
    author={Zhongao Sun, Jiameng Li, Yuhan Wang, Jiarong Cheng, Qing Zhou, and
  Chun Li},
    journal={arXiv preprint arXiv:2406.08634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08634},
    primaryClass={eess.IV cs.CV cs.LG}
}