# Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks

arXiv: https://arxiv.org/abs/2406.03901 · HTML: https://mlsanity.com/paper/2406.03901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy](https://mlsanity.com/paper/2011.08065/) (2020)
- [The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models](https://mlsanity.com/paper/2009.01907/) (2020)
- [Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation](https://mlsanity.com/paper/2110.01939/) (2021)
- [Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy](https://mlsanity.com/paper/2107.02319/) (2021)

## Abstract (arXiv)

This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts.