A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

arXiv:2404.14019 · cs.CV, eess.SP, stat.AP · Submitted 2024-04-22 · Read on arXiv

cs.CV, eess.SP, stat.AP

Submitted: 2024-04-22

Updated: 2026-09-27

Terminology

• Transformer — 78× in this paper · explained in 2 episodes
• Distillation — 59× in this paper · explained in 2 episodes
• Multimodal Feature Distillation — 16× in this paper
• Knowledge Distillation — 12× in this paper · explained in 11 episodes
• Loss Function — 12× in this paper · explained in 2 episodes
• Cross-Modal Fusion — 11× in this paper
• Ablation — 10× in this paper · explained in 1 episode
• Self-distillation — 7× in this paper · explained in 6 episodes
• Ablation study — 6× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Multimodal Learning — 6× in this paper · explained in 2 episodes
• Feature Extraction — 5× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Core — 4× in this paper · explained in 2 episodes
• Convolutional Neural Network — 3× in this paper
• Hausdorff Distance — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Unimodal Feature Enhancement — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Generalized Surface Loss — 2× in this paper
• Hybrid Architectures — 2× in this paper · explained in 3 episodes
• Magnetic Resonance Imaging — 2× in this paper
• Resource Constraints — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computed Tomography — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Mean Squared Error — 1× in this paper
• Meta-learning — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• State Space Modeling — 1× in this paper
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Mamba in Vision: A Comprehensive Survey of Techniques and Applications
• A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
• Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning
• Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency
• MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment
• Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using H\"older Divergence and Mutual Information-Enhanced Knowledge Transfer
• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

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