解决医学图像分割中特征不平衡问题，提升精度
FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention
- 双路编码器分别提取全局与局部特征，动态融合避免信息失衡
- 混合注意力机制结合自注意力与蒙特卡洛注意力，精准捕捉细节与大范围依赖
- 在多器官和心脏数据集上表现优于主流方法，适合临床高精度分割场景
随着视觉变压器在计算机视觉中的广泛应用，融合卷积神经网络（CNN）与变压器的混合架构在医学图像分割任务中展现出良好性能。然而，直接融合来自CNN与变压器的特征常导致特征不平衡和冗余信息。为此，本文提出一种特征不平衡感知的分割网络（FIAS），包含双分支编码器与新型混合注意力（MixAtt）解码器。双分支编码器结合了用于长程全局特征提取的DilateFormer与用于捕捉细粒度局部细节的深度可分离多核（DMK）卷积。上下文感知融合（CAF）模块动态调节全局与局部特征的贡献，防止特征失衡。MixAtt解码器通过融合自注意力与蒙特卡洛注意力，进一步提升分割精度，有效捕捉小尺度细节与大范围依赖关系。在Synapse多器官与ACDC心脏数据集上的实验结果表明，该方法在医学图像分割任务中具有强竞争力。
原文摘要 · Abstract (English)
With the growing application of transformer in computer vision, hybrid architecture that combine convolutional neural networks (CNNs) and transformers demonstrates competitive ability in medical image segmentation. However, direct fusion of features from CNNs and transformers often leads to feature imbalance and redundant information. To address these issues, we propose a Feaure Imbalance-Aware Segmentation (FIAS) network, which incorporates a dual-path encoder and a novel Mixing Attention (MixAtt) decoder. The dual-branches encoder integrates a DilateFormer for long-range global feature extraction and a Depthwise Multi-Kernel (DMK) convolution for capturing fine-grained local details. A Context-Aware Fusion (CAF) block dynamically balances the contribution of these global and local features, preventing feature imbalance. The MixAtt decoder further enhances segmentation accuracy by combining self-attention and Monte Carlo attention, enabling the model to capture both small details and large-scale dependencies. Experimental results on the Synapse multi-organ and ACDC datasets demonstrate the strong competitiveness of our approach in medical image segmentation tasks.
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