  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
https://github.com/haoshao-nku/medical_seg.git.
