We present a new boundary sensitive framework for polyp segmentation, called
Polyper. Our method is motivated by a clinical approach that seasoned medical
practitioners often leverage the inherent features of interior polyp regions to
tackle blurred boundaries.Inspired by this, we propose explicitly leveraging
polyp regions to bolster the model's boundary discrimination capability while
minimizing computation. Our approach first extracts boundary and polyp regions
from the initial segmentation map through morphological operators. Then, we
design the boundary sensitive attention that concentrates on augmenting the
features near the boundary regions using the interior polyp regions's
characteristics to generate good segmentation results. Our proposed method can
be seamlessly integrated with classical encoder networks, like ResNet-50,
MiT-B1, and Swin Transformer. To evaluate the effectiveness of Polyper, we
conduct experiments on five publicly available challenging datasets, and
receive state-of-the-art performance on all of them. Code is available at
this https URL

我们提出了一种新的针对息肉分割的边界敏感框架 Polyper，该方法通过使用形态操作符从初始分割图中提取边界和息肉区域，设计了边界敏感注意力机制，集中于利用内部息肉区域的特征增强边界附近的特征，以生成良好的分割结果，并在多个数据集上达到了最先进的性能。