  Accurate polyp segmentation is of great importance for colorectal cancer
diagnosis and treatment. However, due to the high cost of producing accurate
mask annotations, existing polyp segmentation methods suffer from severe data
shortage and impaired model generalization. Reversely, coarse polyp bounding
box annotations are more accessible. Thus, in this paper, we propose a boosted
BoxPolyp model to make full use of both accurate mask and extra coarse box
annotations. In practice, box annotations are applied to alleviate the
over-fitting issue of previous polyp segmentation models, which generate
fine-grained polyp area through the iterative boosted segmentation model. To
achieve this goal, a fusion filter sampling (FFS) module is firstly proposed to
generate pixel-wise pseudo labels from box annotations with less noise, leading
to significant performance improvements. Besides, considering the appearance
consistency of the same polyp, an image consistency (IC) loss is designed. Such
IC loss explicitly narrows the distance between features extracted by two
different networks, which improves the robustness of the model. Note that our
BoxPolyp is a plug-and-play model, which can be merged into any appealing
backbone. Quantitative and qualitative experimental results on five challenging
benchmarks confirm that our proposed model outperforms previous
state-of-the-art methods by a large margin.
