mlsanityData to 7 Oct 2026

XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder

arXiv 2412.07804 ↗First posted 9 Dec 2024eess.IV cs.AI cs.CVcited by 5

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Abstract (arXiv)

Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging techniques often compromises the accuracy of tumor segmentation during the imaging process. To address this issue, we introduce the XLSTM-HVED model. This model integrates a hetero-modal encoder-decoder framework with the Vision XLSTM module to reconstruct missing MRI modalities. By deeply fusing spatial and temporal features, it enhances tumor segmentation performance. The key innovation of our approach is the Self-Attention Variational Encoder (SAVE) module, which improves the integration of modal features. Additionally, it optimizes the interaction of features between segmentation and reconstruction tasks through the Squeeze-Fusion-Excitation Cross Awareness (SFECA) module. Our experiments using the BraTS 2024 dataset demonstrate that our model significantly outperforms existing advanced methods in handling cases where modalities are missing. Our source code is available at https://github.com/Quanato607/XLSTM-HVED.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

9 of 14 references.

References · 9

  1. 2018Multimodal Generative Models for Scalable Weakly-Supervised Learningcited by 200
  2. 2019Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Networkcited by 98
  3. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 94
  4. 2019Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentationcited by 68
  5. 2020Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusioncited by 67
  6. 2014Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictionscited by 66
  7. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 65
  8. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  9. 2024Vision-LSTM: xLSTM as Generic Vision Backbonecited by 51

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentationcited by 5
  2. 2025No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentationcited by 4
  3. 2025AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRIcited by 1
  4. 2025An overview of neural architectures for self-supervised audio representation learning from masked spectrogramscited by 0
  5. 2026D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalitiescited by 0