In this study, we propose MHEX+, a framework adaptable to any U-Net architecture. Built upon MHEX+, we introduce novel U-Net variants, EU-Nets, which enhance explainability and uncertainty estimation, addressing the limitations of traditional U-Net models while improving performance and stability. A key innovation is the Equivalent Convolutional Kernel, which unifies consecutive convolutional layers, boosting interpretability. For uncertainty estimation, we propose the collaboration gradient approach, measuring gradient consistency across decoder layers. Notably, EU-Nets achieve an average accuracy improvement of 1.389\% and a variance reduction of 0.83\% across all networks and datasets in our experiments, requiring fewer than 0.1M parameters.

翻译：本研究提出MHEX+框架，该框架可适配于任何U-Net架构。基于MHEX+，我们引入新型U-Net变体——EU-Nets，其在提升性能与稳定性的同时，增强了模型的可解释性与不确定性估计能力，有效解决了传统U-Net模型的局限性。核心创新在于等效卷积核的提出，该机制通过统一连续卷积层显著提升了模型可解释性。针对不确定性估计，我们提出协作梯度方法，通过度量解码器各层间的梯度一致性实现量化评估。值得注意的是，在全部网络架构与实验数据集上，EU-Nets仅需不足0.1M参数即可实现平均精度提升1.389%，方差降低0.83%。