# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 1 Introduction
Based on the lack of research on the propagation of complementary multi-level aggregated information, we studied the relationship between low- and high-level representations and focused on the propagation of complementary information. Another approach is the use of boundary information to compensate for insufficient polyp representation. Psi-Net [23] addressed a joint training strategy using polyp region and boundary detection tasks. Moreover, SFANet [20] incorporated boundary-sensitive loss with boundary deep supervision maps [24] to detect polyp boundaries more elaborately. In terms of boundary refinement, MSBNet [25] utilized a low-level representation with a Gaussian kernel to enhance boundary information at the highest representation. Another study [26] organized multiple boundary attention modules designed to discriminate boundary information using encoder and decoder representations adjacent to each other at each decoder. In PraNet [27], which improved the model efficiency and outperformed existing studies, a parallel reverse attention method with partial decoders was employed to incorporate the polyp area and boundary features [15; 28]. Although PraNet considered the region and boundaries, the reverse attention method could not sufficiently discriminate the boundary from the background. Because the boundary is obtained by aggregating the high-level encoder outputs, which present the abstracted region information, the reversed region contains insufficient boundaries compared to low-level outputs. In previous studies, the explicit boundary information can be propagated across the decoders to detect the polyp boundaries more elaborately, although the boundary was either used independently in a single decoder or not proliferated. Therefore, we focused on propagating the explicit boundary by employing a multi-decoder structure. In medical image segmentation tasks, binary cross entropy (BCE) and IoU losses are commonly used to deal with local and global structural awareness. The BCE and IoU losses cause a class imbalance problem because they treat all pixels with equal importance. To reduce this problem, weighted BCE (wBCE) and IoU (wIoU) losses have been proposed in previous studies on salient object detection tasks [29]. In polyp segmentation, PraNet [27] demonstrated state-of-the-art performance by adopting wBCE and wIoU losses. In addition, we consider an L1 distance loss, which can handle noisy annotations [30,31] often encountered in segmentation tasks. Thus, we combine the wBCE, wIoU, and L1 loss functions to treat class imbalance and noisy label problems. This study proposes a propagating complementary multi-level aggregation network (COMMA), which comprises a complementary masking module (CMM) and boundary propagation module (BPM) as the multi-decoder structure. The CMM clarifies the boundary noise in the low-level through the abstracted high-level representation and propagates the refined information to another decoder. The BPM generates an explicit boundary by masking the lowest-level representation through the highest-level outputs. The boundary is propagated to the CMMs in the next decoder to enhance the segmentation performance. We also propose a hybrid loss function that allows the network to learn robustly on noisy labels and assigns different importance to each pixel. The main contributions of this study are as follows: First, we propose complementary multi-level aggregation, which contributes to reducing the multi-level distribution discrepancy by applying the abstracted high-level representation as a mask to the low-level boundary noises and propagating the complementary information. Second, the explicit boundary propagation for the multi-decoder discriminates polyps in an ambiguous context and enhances the segmentation performance. Third, we design a hybrid loss function consisting of weighted BCE, weighted IoU, and L1 distance loss. The hybrid loss function enables the model to focus on relatively important pixels and to be robust against noisy annotations.