# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 1 Introduction  
Colorectal cancer (CRC), which is one of the most common cancers globally, usually begins as a polyp in the colon mucosa, and approximately one-quarter of untreated polyps can develop into colon cancer [1]. Early polyp detection is a significant task in preventing CRC, and colonoscopy is used extensively as a standard polyp detection method [2, 3, 4]. Although colonoscopy is an effective method for detecting polyps at the early stages, polyp detection using colonoscopy images is a challenging task owing to the ambiguous image context. As polyps are usually small and their boundaries are low in contrast to their surroundings, polyps can easily be mistaken for wrinkles or other intestinal structures, leading to inaccurate segmentation and over-segmentation. Therefore, discrimination of the precise polyp region from an ambiguous context is critical for improving early polyp detection and preventing CRC. Based on the need for elaborate segmentation, early studies utilized handcrafted features with a classifier [5, 6]. However, the handcrafted approaches suffer from unsatisfactoryperformance because they cannot cover both intra- and inter-class variations [7]. Recent studies have proposed deep learning-based approaches, including fully convolutional networks (FCNs) [8] and U-Net [9]. Moreover, as alternative approach, Mask R-CNN [10] based models have also been proposed for precise polyp segmentation [11]. In contrast to existing CNN-based networks, PNS-Net [12] introduced a normalized self-attention network that dynamically operated the receptive field of the network. Along with the network architecture, a recent approach [13] considered excluding the effect of colors because polyp images are gathered under varying conditions. By alleviating discrepancies in color distribution, they improved the generalization performance of the network. The U-Net comprises encoder-decoder structures that aggregate multiple encoder representations at the decoder to overcome the insufficient representations of the handcrafted approaches. Although the encoder-decoder structure improves the polyp segmentation performance, this structure exhibits a distribution discrepancy between the low- and high-level representations when aggregating multi-level features. Because the low-level representation contains sufficient boundary information with background noise, whereas the high-level feature presents abstracted region information [14; 15; 16]. To overcome the drawbacks of both representations, each level requires the elimination of noise and an emphasis on boundary information. Without the denoising and refining fine information, the multi-level aggregation discrepancy occurs and it generates rough boundaries with noise in the prediction map, which limits the model performance. To reduce this discrepancy, U-Net++ [17] proposed multiple convolution layers on the skip pathways between the encoder and decoder features. A previous study [18] improved on existing U-Net++-based architecture by applying conditional random field and test-time augmentation to the ResUNet++ [19] for precise polyp segmentation. Focusing more on the multi-level aggregation problem, previous studies [20; 21] organized the network including selective feature extraction and aggregation using multiple kernel sizes. In addition, Enhanced U-Net [22] introduced attention modules designed to extract distinct features from the highest-level representation with different patch sizes and to refine the encoder features by utilizing the distinct features. Similarly, SANet [13] proposed fusing both low- and high-level representations to detect fine polyps by excluding background noise in low-level representations with abstracted high-level information. These existing studies contributed to diminishing the multi-level distribution discrepancy; however, they did not propagate complementary low- and high-level aggregation to decoder structure to clarify polyp representation.