  Besides the complex nature of colonoscopy frames with intrinsic frame
formation artefacts such as light reflections and the diversity of polyp
types/shapes, the publicly available polyp segmentation training datasets are
limited, small and imbalanced. In this case, the automated polyp segmentation
using a deep neural network remains an open challenge due to the overfitting of
training on small datasets. We proposed a simple yet effective polyp
segmentation pipeline that couples the segmentation (FCN) and classification
(CNN) tasks. We find the effectiveness of interactive weight transfer between
dense and coarse vision tasks that mitigates the overfitting in learning. And
It motivates us to design a new training scheme within our segmentation
pipeline. Our method is evaluated on CVC-EndoSceneStill and Kvasir-SEG
datasets. It achieves 4.34% and 5.70% Polyp-IoU improvements compared to the
state-of-the-art methods on the EndoSceneStill and Kvasir-SEG datasets,
respectively.
