SelfReg-UNet: 用于医学图像分割的自正则化UNet
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
AI总结：
针对UNet因非对称监督产生无关特征、特征图存在冗余的问题，提出SelfReg-UNet，通过解码器末层额外监督与特征蒸馏优化，可即插即用提升UNet在医学图像分割任务上的性能。
摘要：
自问世以来，UNet一直在各类医学图像分割任务中处于领先地位。尽管后续已有大量研究致力于提升标准UNet的性能，但鲜有研究深入分析UNet在医学图像分割中的底层兴趣模式。本文探究了UNet中学习到的模式，并观察到两个可能影响其性能的重要因素：（i）由非对称监督导致的无关特征学习；（ii）特征图中的特征冗余。为此，我们提出平衡编码器与解码器之间的监督，并减少UNet中的冗余信息。具体而言，我们利用包含最多语义信息的特征图（即解码器的最后一层）为其他模块提供额外监督，并通过特征蒸馏减少特征冗余。该方法可以即插即用的方式轻松集成到现有UNet架构中，计算成本可忽略不计。实验结果表明，所提方法在四个医学图像分割数据集上持续提升了标准UNet的性能。代码可在https://github.com/ChongQingNoSubway/SelfReg-UNet获取。
英文摘要：
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performance of standard UNet, few have conducted in-depth analyses of the underlying interest pattern of UNet in medical image segmentation. In this paper, we explore the patterns learned in a UNet and observe two important factors that potentially affect its performance: (i) irrelative feature learned caused by asymmetric supervision; (ii) feature redundancy in the feature map. To this end, we propose to balance the supervision between encoder and decoder and reduce the redundant information in the UNet. Specifically, we use the feature map that contains the most semantic information (i.e., the last layer of the decoder) to provide additional supervision to other blocks to provide additional supervision and reduce feature redundancy by leveraging feature distillation. The proposed method can be easily integrated into existing UNet architecture in a plug-and-play fashion with negligible computational cost. The experimental results suggest that the proposed method consistently improves the performance of standard UNets on four medical image segmentation datasets. The code is available at \url{https://github.com/ChongQingNoSubway/SelfReg-UNet}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
