# Generative Adversarial Networks for Automatic Polyp Segmentation

arXiv: https://arxiv.org/abs/2012.06771 · HTML: https://mlsanity.com/paper/2012.06771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images](https://mlsanity.com/paper/2209.00729/) (2022)
- [Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges](https://mlsanity.com/paper/2307.16262/) (2023)
- [PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integration](https://mlsanity.com/paper/2409.08501/) (2024)
- [EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling](https://mlsanity.com/paper/2405.11846/) (2024)

## Abstract (arXiv)

This paper 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.