mlsanityData to 7 Oct 2026

Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities

arXiv 2104.06231 ↗First posted 13 Apr 2021eess.IV cs.CVcited by 42

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to miss some imaging modalities in clinical practice. In this paper, we present a novel brain tumor segmentation algorithm with missing modalities. Since it exists a strong correlation between multi-modalities, a correlation model is proposed to specially represent the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modality. First, the individual representation produced by each encoder is used to estimate the modality independent parameter. Then, the correlation model transforms all the individual representations to the latent multi-source correlation representations. Finally, the correlation representations across modalities are fused via attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 and BraTS 2019 dataset, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 13 Apr 2021.

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25 of 45 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  4. 2018Dual Attention Network for Scene Segmentationcited by 1,001
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  7. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
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13 more
  1. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  2. 2019Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentationcited by 68
  3. 2020Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusioncited by 67
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  5. 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
  6. 2019Brain Tumor Segmentation on MRI with Missing Modalitiescited by 42
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  8. 2019One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentationcited by 25
  9. 2019A unified representation network for segmentation with missing modalitiescited by 15
  10. 2019Brain Tumor Segmentation and Survival Predictioncited by 10
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Citing papers per year: 2021: 1; 2022: 10; 2023: 8; 2024: 13; 2025: 4; 2026: 6.

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  1. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 89
  2. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  3. 2023Unified Multi-Modal Image Synthesis for Missing Modality Imputationcited by 39
  4. 2022SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiescited by 31
  5. 2023Prototype Knowledge Distillation for Medical Segmentation with Missing Modalitycited by 24
  6. 2022Medical Image Segmentation on MRI Images with Missing Modalities: A Reviewcited by 18
  7. 2022SFusion: Self-attention based N-to-One Multimodal Fusion Blockcited by 14
  8. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  9. 2021Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalitiescited by 13
  10. 2024Gradient-Guided Modality Decoupling for Missing-Modality Robustnesscited by 11
  11. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  12. 2024Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentationcited by 8
30 more
  1. 2025Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalitiescited by 7
  2. 20243D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimescited by 6
  3. 2024MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Trainingcited by 6
  4. 2024Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Datacited by 5
  5. 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalitiescited by 5
  6. 2024Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insightscited by 4
  7. 2022ISA-Net: Improved spatial attention network for PET-CT tumor segmentationcited by 3
  8. 2023Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomographycited by 3
  9. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  10. 2022Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusioncited by 2
  11. 2022A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Predictioncited by 2
  12. 2024Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalitiescited by 2
  13. 2022DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Networkcited by 1
  14. 2022Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRIcited by 1
  15. 2023TKN: Transformer-based Keypoint Prediction Network For Real-time Video Predictioncited by 1
  16. 2024A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalitiescited by 1
  17. 2022A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentationcited by 0
  18. 2022Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learningcited by 0
  19. 2023Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learningcited by 0
  20. 2023Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentationcited by 0
  21. 2024Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translationcited by 0
  22. 2024Machine Learning Techniques for MRI Data Processing at Expanding Scalecited by 0
  23. 2025UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusioncited by 0
  24. 2025DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalitiescited by 0
  25. 2025Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methodscited by 0
  26. 2026Hyper-Connections for Adaptive Multi-Modal MRI Brain Tumor Segmentationcited by 0
  27. 2026Multicentric thrombus segmentation using an attention-based recurrent network with gradual modality dropoutcited by 0
  28. 2026Resilient Vision-Tabular Multimodal Learning under Modality Missingnesscited by 0
  29. 2026D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalitiescited by 0
  30. 2026Context-driven Missing-Modality Learning for Robust Medical Diagnosis with Image-Tabular Datacited by 0