mlsanityData to 7 Oct 2026

Brain Tumor Segmentation on MRI with Missing Modalities

arXiv 1904.07290 ↗First posted 15 Apr 2019cs.CVcited by 42

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Abstract (arXiv)

Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing channel. Our results demonstrate that the quality of the segmentation depends on which modality is missing. Furthermore, we also discuss and visualize the contribution of each modality to the segmentation results. Their contributions are along well with the expert screening routine.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 9; 2024: 11; 2025: 7; 2026: 5.

42 papers.

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  1. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 159
  2. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 94
  3. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 89
  4. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Studycited by 64
  5. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  6. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 51
  7. 2023Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modalitycited by 44
  8. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  9. 2025A Closer Look at Multimodal Representation Collapsecited by 25
  10. 2023Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentationcited by 24
  11. 2021Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Reviewcited by 19
  12. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
30 more
  1. 2022Medical Image Segmentation on MRI Images with Missing Modalities: A Reviewcited by 18
  2. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  3. 2021Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalitiescited by 13
  4. 2024Gradient-Guided Modality Decoupling for Missing-Modality Robustnesscited by 11
  5. 2022Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentationcited by 10
  6. 2023CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalitiescited by 10
  7. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  8. 2023Dynamic Enhancement Network for Partial Multi-modality Person Re-identificationcited by 9
  9. 2024Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentationcited by 8
  10. 2024Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Datacited by 5
  11. 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalitiescited by 5
  12. 2025Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learningcited by 4
  13. 2025Multimodal Masked Autoencoder Pre-training for 3D MRI-Based Brain Tumor Analysis with Missing Modalitiescited by 3
  14. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  15. 2021Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentationcited by 2
  16. 2022DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Networkcited by 1
  17. 2024A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalitiescited by 1
  18. 2025DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalitiescited by 1
  19. 2026SRUG: A Fusion-Driven Generator Network for Medical Image Translationcited by 1
  20. 2021Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learningcited by 0
  21. 2023ReFuSeg: Regularized Multi-Modal Fusion for Precise Brain Tumour Segmentationcited by 0
  22. 2024MDA: An Interpretable and Scalable Multi-Modal Fusion under Missing Modalities and Intrinsic Noise Conditionscited by 0
  23. 2024Full Information Linked ICA: addressing missing data problem in multimodal fusioncited by 0
  24. 2024QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Predictioncited by 0
  25. 2025Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidancecited by 0
  26. 2025F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirementcited by 0
  27. 2025TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classificationcited by 0
  28. 2026Brain MR Image Synthesis with 3D Multi-Contrast Self-Attention GANcited by 0
  29. 2026Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalitiescited by 0
  30. 2026Unified Multimodal Model for Brain MRI Imputation and Understandingcited by 0