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Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation

arXiv 2503.16149 ↗First posted 20 Mar 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

Efficient modal feature fusion strategy is the key to achieve accurate segmentation of brain glioma. However, due to the specificity of different MRI modes, it is difficult to carry out cross-modal fusion with large differences in modal features, resulting in the model ignoring rich feature information. On the other hand, the problem of multi-modal feature redundancy interaction occurs in parallel networks due to the proliferation of feature dimensions, further increase the difficulty of multi-modal feature fusion at the bottom end. In order to solve the above problems, we propose a noval complementary feature compression interaction network (CFCI-Net), which realizes the complementary fusion and compression interaction of multi-modal feature information with an efficient mode fusion strategy. Firstly, we propose a selective complementary feature fusion (SCFF) module, which adaptively fuses rich cross-modal feature information by complementary soft selection weights. Secondly, a modal feature compression interaction (MFCI) transformer is proposed to deal with the multi-mode fusion redundancy problem when the feature dimension surges. The MFCI transformer is composed of modal feature compression (MFC) and modal feature interaction (MFI) to realize redundancy feature compression and multi-mode feature interactive learning. %In MFI, we propose a hierarchical interactive attention mechanism based on multi-head attention. Evaluations on the BraTS2019 and BraTS2020 datasets demonstrate that CFCI-Net achieves superior results compared to state-of-the-art models. Code: https://github.com/CDmm0/CFCI-Net

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 59 references that are in the map. First posted on arXiv 20 Mar 2025.

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34 of 59 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  9. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  10. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  11. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  12. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
22 more
  1. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
  2. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  3. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  4. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  5. 2016HeMIS: Hetero-Modal Image Segmentationcited by 102
  6. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 94
  7. 2020A review: Deep learning for medical image segmentation using multi-modality fusioncited by 84
  8. 2020Multi-Head Attention: Collaborate Instead of Concatenatecited by 56
  9. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
  10. 2021A Robust Volumetric Transformer for Accurate 3D Tumor Segmentationcited by 41
  11. 2022NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentationcited by 33
  12. 2019One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentationcited by 25
  13. 2022CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentationcited by 24
  14. 2022Cross-Modality Deep Feature Learning for Brain Tumor Segmentationcited by 22
  15. 2021BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentationcited by 19
  16. 2022Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentationcited by 11
  17. 2021A Tri-attention Fusion Guided Multi-modal Segmentation Networkcited by 10
  18. 2021Prior Attention Network for Multi-Lesion Segmentation in Medical Imagescited by 9
  19. 2019TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networkscited by 8
  20. 2020Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentationcited by 8
  21. 2023Hybrid-Fusion Transformer for Multisequence MRIcited by 6
  22. 2020Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraintcited by 5

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