## 3 Materials and Methods
### Efficient Atrous Convolution Attention Module  
A traditional convolutional layer often incorporates down-sampling operations, which can effectively expand the receptive field but decrease the spatial size of feature maps. This can indeed result in the loss of spatial hierarchical information, which could be detrimental, especially when reconstructing small objects like polyps. The atrous convolution proposed by [16; 31] effectively addresses this issue by inserting a gap between the convolution kernels to increase the receptive field. The key parameter dilation rate in the atrous convolution indicates that the convolution kernel is filled with (dilation rate-1) zeros. To capture the image context at multiple scales, the atrous convolution with various dilation rates is applied in parallel to a given input, and the resulting outputs are concatenated in the channel dimension. Subsequently, a \(1\times 1\) convolution is used to reduce the number of channels to the desired value.  
**EACA module:** Figure 3 shows the architecture of this module. The architecture consists of three parts: (1) An atrous spatial pyramid, which consists of several convolutions: one \(3\times 3\) normal convolution is used to perform the local feature extractor, which is inside the yellow dashed box, and three \(3\times 3\) atrous spatial convolutions in the red dashed box, with dilation rates of 1, 2, and 3, are set to increase the perceptual field, acting as the surrounding context extractor. (2) Joint feature extractor: after concatenating the feature maps output from the atrous spatial pyramid, the resultant is passed through batch normalization (BN) and the parametric rectified linear unit (PReLU) to speed up the convergence and alleviate the problem of a potential vanishing gradient in the deep network to a certain extent, corresponding to the blue dashed box. (3) Global feature extractor: the global average pooling is first used as a weighted vector for fine-tuning the joint features channel by channel to emphasize the useful elements and compress the useless ones; then, two layers of fast one-dimensional convolution are used to generate channel attention so that the global context is further extracted based on no dimension reduction; finally, a scale layer is used as the subsequent layer of BN to scale and shift the normalized parameters, re-weight the joint features and correct the features, and retain  
Figure 2: Paralleled partial decoder structure.  
the valuable features to achieve the purpose of improving the feature expression ability, corresponding to the purple dashed box.  
**MFA mechanism**: Additionally, in traditional target detection, typically only the last layer's output features are utilized for object classification and localization. This approach can compromise the detection capability, especially for small target objects; this is commonly referred to as the multi-scale problem. Consequently, it is imperative to gather information from various scales to comprehend the intricacies of tasks such as fine-grained classification and semantic segmentation. Hence, we propose a multi-level feature aggregation (MFA) mechanism to consolidate multi-scale information across different levels. As depicted in Figure 1, the feature \(f_{i}\) output by the convolution of this layer and the output \(f_{i-1}^{{}^{\prime}}\) by the upper layer EACA module are concatenated to obtain the joint feature, and then the joint feature is input into the EACA module of this layer. The multi-level feature aggregation mechanism offers a feature aggregation mechanism with minimal parameter overhead, enabling the transfer of detailed information from lower levels to the top layer, thereby augmenting the high-level details and enhancing the ability to detect multi-scale targets. Remarkably, the computational and memory overhead incurred by concatenation is nearly negligible.