# Refined Deep Neural Network and U-Net for Polyps Segmentation

arXiv: https://arxiv.org/abs/2105.14848 · HTML: https://mlsanity.com/paper/2105.14848/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation](https://mlsanity.com/paper/2012.15244/) (2020)

## Built on by (most cited first, 2 of 2)

- [Meta-Polyp: a baseline for efficient Polyp segmentation](https://mlsanity.com/paper/2305.07848/) (2023)
- [Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges](https://mlsanity.com/paper/2307.16262/) (2023)

## Abstract (arXiv)

The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided diagnosis system for automatic segmentation [3]. We participate in task 1, Polyps segmentation task, which is to develop algorithms for segmenting polyps on a comprehensive dataset. 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 various types of polyps in endoscopic images. We select 5 runs with different architecture and parameters in our methods. Our methods show potential results in accuracy and efficiency through multiple experiments, and our team is in the Top 3 best results with a Jaccard index of 0.765.