HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation

In the EndoCV2021 challenge, aiming to enhance PraNet [1], we utilized a parallel res2Net-based network with reverse attention and proposed a new post-processing workflow to

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

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 summary, the Medico 2020 challenge can support building fu- ture systems and foster open, comparable and reproducible results where the objective of the task is to find

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