Real-Time Polyp Segmentation Using U-Net with IoU Loss

Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. Brox, U-net:

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

For the detection task, we perform joint optimization of classification and regression with adaptive training sample selection strategies in order to deal with the

In [19], state-of-the-art deep learning methods for polyp detection, localization, and segmentation were compared on the basis of both accuracy and speed with extensive experiments

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..

In this work, the fine tuning of pretrained UNet model with atten- tion mechanism has shown some promising results.. This approach removes the requirement of an external

In this task, we propose methods combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model and PraNet for semantic segmentation of

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