Automatic polyp segmentation via parallel reverse attention network

In this paper, we present a novel deep neural network, termed Parallel Reverse Attention Network (PraNet), for the task of automatic polyp segmentation at MediaEval 2020. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using the reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating misaligned predictions, improving the segmentation accuracy and achieving real-time efficiency (∼30fps) on a single NVIDIA GeForce GTX 1080 GPU.