提出一种保留图像明暗对比的新型下采样方法，提升医学图像分割精度。
A Novel Downsampling Strategy Based on Information Complementarity for Medical Image Segmentation
- 用最小最大池化替代传统下采样，融合局部最大值信息
- 在ACDC和Synapse数据集上平均提升0.5%的DSC系数
- 适合对细节敏感的医学图像分割任务
在卷积神经网络中，下采样操作对模型性能至关重要。尽管传统方法（如最大池化和跨行卷积）在特征聚合、感受野扩展和计算量降低方面表现良好，但在语义分割任务中可能造成关键空间信息丢失，影响像素级预测精度。为此，本文提出基于信息互补性的下采样方法——混合池化下采样（HPD）。其核心是用最小最大池化替代传统方法，通过提取局部区域的最大值信息，有效保留图像的明暗对比与细节特征。在ACDC和Synapse多个CNN架构上的实验表明，HPD在分割性能上优于传统方法，平均提升0.5%的Dice相似系数（DSC）。结果证明，HPD模块为语义分割任务提供了一种高效解决方案。
原文摘要 · Abstract (English)
In convolutional neural networks (CNNs), downsampling operations are crucial to model performance. Although traditional downsampling methods (such as maximum pooling and cross-row convolution) perform well in feature aggregation, receptive field expansion, and computational reduction, they may lead to the loss of key spatial information in semantic segmentation tasks, thereby affecting the pixel-by-pixel prediction accuracy.To this end, this study proposes a downsampling method based on information complementarity - Hybrid Pooling Downsampling (HPD). The core is to replace the traditional method with MinMaxPooling, and effectively retain the light and dark contrast and detail features of the image by extracting the maximum value information of the local area.Experiment on various CNN architectures on the ACDC and Synapse datasets show that HPD outperforms traditional methods in segmentation performance, and increases the DSC coefficient by 0.5% on average. The results show that the HPD module provides an efficient solution for semantic segmentation tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。