# Automatic Polyp Segmentation using U-Net-ResNet50

arXiv: https://arxiv.org/abs/2012.15247 · HTML: https://mlsanity.com/paper/2012.15247/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 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)
- [2020 CATARACTS Semantic Segmentation Challenge](https://mlsanity.com/paper/2110.10965/) (2021)
- [An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products](https://mlsanity.com/paper/2107.11643/) (2021)
- [Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges](https://mlsanity.com/paper/2307.16262/) (2023)
- [DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation](https://mlsanity.com/paper/2304.14571/) (2023)

## Abstract (arXiv)

Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.