用注意力和多尺度融合改进嵌套U-Net，提升生物标志物分割准确率
Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion
- 引入多尺度特征融合与注意力机制增强编码器特征提取
- 在四个数据集上超越现有最佳方法，平均Dice达0.917
- 适合需要高精度医学图像分割的研究者使用
在生物技术应用中，识别医学图像中的生物标志物至关重要。然而，近期基于Transformer和CNN的方法常因形态与染色差异而难以有效提取特征。在医学图像分割任务中，由于样本有限，当前最优方法通常依赖预训练编码器，而端到端方法则因编码器与解码器间多尺度特征传递困难而表现不佳。为此，我们提出一种嵌套U-Net架构，通过多尺度特征融合与注意力机制捕捉局部与全局上下文信息。该设计提升了编码器特征整合能力，突出关键通道与区域，并恢复空间细节，显著增强分割性能。实验在四个数据集上验证了其优越性，并通过详尽的消融研究证明有效性。代码已开源。
原文摘要 · Abstract (English)
Identifying biomarkers in medical images is vital for a wide range of biotech applications. However, recent Transformer and CNN based methods often struggle with variations in morphology and staining, which limits their feature extraction capabilities. In medical image segmentation, where data samples are often limited, state-of-the-art (SOTA) methods improve accuracy by using pre-trained encoders, while end-to-end approaches typically fall short due to difficulties in transferring multiscale features effectively between encoders and decoders. To handle these challenges, we introduce a nested UNet architecture that captures both local and global context through Multiscale Feature Fusion and Attention Mechanisms. This design improves feature integration from encoders, highlights key channels and regions, and restores spatial details to enhance segmentation performance. Our method surpasses SOTA approaches, as evidenced by experiments across four datasets and detailed ablation studies. Code: https://github.com/saadwazir/ReN-UNet
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