提出新型融合网络，提升脑肿瘤多模态MRI分割精度。
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation
- 通过自适应选择性互补融合模块，精准整合不同MRI模态特征。
- 在BraTS2019/2020上达到新最优，分割性能超越现有模型。
- 适合医学图像分析、多模态深度学习研究者参考。
高效模态特征融合是实现脑胶质瘤精准分割的关键。然而，由于不同MRI模态特征差异显著，跨模态融合困难，导致模型忽略丰富特征信息。同时，平行网络中特征维度激增引发多模态特征冗余交互问题，进一步加剧底层特征融合难度。为此，本文提出一种新颖的互补特征压缩交互网络（CFCI-Net），通过高效模态融合策略实现多模态特征的互补融合与压缩交互。首先，设计选择性互补特征融合（SCFF）模块，利用互补软选择权重自适应融合跨模态特征信息；其次，提出模态特征压缩交互（MFCI）Transformer，由模态特征压缩（MFC）与模态特征交互（MFI）组成，实现冗余特征压缩与多模态特征交互学习。MFI中引入基于多头注意力的分层交互注意力机制。在BraTS2019和BraTS2020数据集上的评估表明，CFCI-Net优于当前最先进模型。代码已开源：https://github.com/CDmm0/CFCI-Net。
原文摘要 · Abstract (English)
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
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