GANPolypSeg

Abstract

This work aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution neural networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2 score.

Resources:

Publication:

https://arxiv.org/abs/2012.06771

Presentaion Video:

https://www.awadelrahman.com/blog/2021-06-02-My-Talk-in-%26nbsp%3BMedico-challenge%3A%26nbsp%3BGenerative-Adversarial-Networks-for-Automatic-Polyp-Segmentation%26nbsp%3B.html?post_id=14

GAN-based Model

Dataset used:

The Kvasir-SEG Dataset: Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Pål Halvorsen, Dag Johansen, Thomas de Lange, and Håvard D. Johansen, Kvasir-SEG: A Segmented Polyp Dataset, In Proceedings of the ternational conference on Multimedia Modeling, Republic of Korea, 2020.

Result on Test Set