mlsanityData to 14 Oct 2026

Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning

arXiv 2406.08634 ↗First posted 12 Jun 2024eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 56 references that are in the map. First posted on arXiv 12 Jun 2024.

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References · 34

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22 more
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  3. 2024MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment
  4. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
  5. 2025Bridging the Gap in Missing Modalities: Leveraging Knowledge Distillation and Style Matching for Brain Tumor Segmentation
  6. 2025CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
  7. 2024A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities
  8. 2022DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans