Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network (M$^2$UNet) for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods.

翻译：息肉分割近年来引起了广泛关注，多种方法已被提出并取得了令人满意的结果。然而，由于卷积运算的特性，这些技术在处理复杂的息肉前景及其周围区域时常常面临困难。此外，大多数现有方法忽略了从多个解码器阶段挖掘潜在信息。为解决这一挑战，我们建议将作为整合CNN与Transformer基线的MetaFormer与UNet框架结合，并引入我们的多尺度上采样块（MU）。该简单模块通过探索浅层解码器阶段的多个感受野路径，并与高层阶段相加以聚合更好的特征表示，从而能够结合多层级信息，这在医学图像分割中至关重要。综合以上，我们提出了用于息肉分割任务的MetaFormer多尺度上采样网络（M²UNet）。在五个基准数据集上进行的大量实验表明，与先前多种方法相比，我们的方法取得了具有竞争力的性能。