Ensemble U-Net Model for Efficient Polyp Segmentation

Evolution of the Dice loss in validation, for the SegTHOR dataset, in fully supervised setting, using different configuration, from no CoordConv layer to all convolutional

To convert a standard CNN architecture (with fully con- nected layers) to a FCN there are at least three constraints that should be followed so that the FCN produces the exact

In this paper, a novel approach based on a variant of deep Fully Convolutional Network (FCN) with dilated convolutions was presented for handwritten text line seg- mentation..

In this work, we investigate the feasibility of building a generalizable model for polyp segmentation using an ensemble of four MultiResUNet architectures, each trained on

Changing the depth from 50 to 101 increases the quality of the results. This implies that a deeper backbone may always result in better quality. However, our results show that a

Perceiving the problem as an image-to-image transla- tion task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs..

So, motivated by the insights of GANs in the domain of medical image analysis [8], we have proposed a deep conditional adversarial learning based network for automatic

Each encoder block starts with two residual blocks, where each residual block consists of two 3 × 3 convolution and a shortcut connecting the output of both the convolution layer,