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Accurate polyp segmentation during colonoscopy examinations can help the clinicians accurately locate polyp areas for further diagnosis or surgeries and thereby decrease the chances of polyps growing into cancer. Although existing approaches can achieve significant improvement by multi-scale feature learning, attention/contextual augmentation, and deep supervision, polyp segmentation is still far from being solved. Actually, enhancing the capability of feature representation may be an excellent way to improve the polyp segmentation performance. From this perspective, we propose a simple but strong framework over feature pyramid network (FPN), called Feature Augmented Pyramid Networks (FAPN), for accurate polyp segmentation with augmented feature representation. Specifically, FAPN consists of three components: Cross-Embedding Module (CEM), Predictive Calibration Module (PCM), and …