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In the above-named article, we explored the ensemble model using two Mask R-CNNs with the backbones consisting of ResNet50 and ResNet101, respectively, for polyp segmentation in colonoscopy images. The proposed ensemble method has two main stages. First, it generates proposals of the regions where there might be an object based on the given input image. Second, it predicts the class of the object, refines the bounding box, and finally generates a mask in the pixel level of the object based on the first stage proposal. Both stages are connected to the backbone structure. Backbone is a Feature Pyramid Network (FPN) style deep neural network. It consists of a bottom-up pathway, a top-bottom pathway, and lateral connections. AlexNet, VGG, Inception-ResNet-v2, ResNet50, and ResNet101 are some of the popular backbone networks.

Corrections to “Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images”