## 2 Related Works
### Principal Models in Deep Learning Polyp Segmentation
In parallel, transformers [9], originally developed for natural language processing, have shown great potential in computer vision. The groundbreaking work of [PERSON] [13] showed that a pure transformer could achieve SOTA results in image classification, marking a significant milestone in the evolution of these models. TransUNet [14] adapted the ViT architecture into a UNet framework, illustrating the versatility of transformers in the domain of medical image segmentation. Unlike CNNs, transformer architectures are adept at capturing long-range dependencies within data sequences, offering strong global modeling capabilities. This advantage has prompted researchers to increasingly adopt transformer-based methods, resulting in notable progress across a range of medical image segmentation tasks, including polyp segmentation. [PERSON] et al. [41] have devised a hybrid framework that employs a pyramid transformer encoder alongside a residual multiscale (RMS) module within the encoder, facilitating the extraction of comprehensive features. The network also integrates a region-enhanced attention module and a feature aggregation module to improve segmentation performance by directing the network in identifying target regions and boundary cues, as well as efficiently combining features from the encoder and decoder layers. [PERSON] et al. [42] have devised a hybrid framework that employs a fuzzy neural network for preprocessing medical images alongside a parallel combination of Swin Transformer V2 [43] and ResNet50 networks within the encoder, facilitating the extraction of comprehensive features. This approach addresses the fuzziness of medical image boundaries and enhances segmentation performance by leveraging fuzzy logic and wavelet transformation for image enhancement. In the cutting-edge domain of polyp segmentation, there has been a recent surge in research efforts towards hybrid-network-based methods synergizing CNNs with transformers. These innovative approaches are crafted to leverage the capabilities of CNNs for identifying local features and the strengths of transformers for capturing long-range dependencies. One notable example is TransFuse [15], which utilizes a parallel branch scheme that integrates transformer and CNN to capture both global dependencies and fine-grained spatial details simultaneously. [PERSON] et al. [16] have unveiled a novel architecture that merges FCNs with transformers, setting new benchmarks in polyp segmentation outcomes. [PERSON] et al. [44] have developed an ensemble framework that integrates CNNs with transformers for polyp segmentation, ensuring diversity among the ensemble components to enhance segmentation performance. Hybrid-network-based methods, utilizing CNNs for precise local feature detection and transformers for expansive long-term feature analysis, have shown notable success in polyp segmentation. However, a common challenge is merging granular CNN insights with broad transformer perspectives, often requiring complex and tailor-made fusion modules. In response, inspired by the success of MLP-based networks [45; 46; 47; 48; 49] in computer vision, we present an innovative hybrid architecture that combines the parallel encoders from a transformer with a convolutional neural network. This integration is seamlessly complemented by an All-MLP decoder, featuring skip connections that refine the feature fusion workflow and bolster the decoding process.