# Y-Net: A deep Convolutional Neural Network for Polyp Detection

arXiv: https://arxiv.org/abs/1806.01907 · HTML: https://mlsanity.com/paper/1806.01907/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021)
- [Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation](https://mlsanity.com/paper/2204.07613/) (2022)
- [Quiver neural networks](https://mlsanity.com/paper/2207.12773/) (2022)
- [From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels](https://mlsanity.com/paper/2206.05288/) (2022)
- [Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging](https://mlsanity.com/paper/2409.18303/) (2024)
- [WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging](https://mlsanity.com/paper/2410.00746/) (2024)
- [Fast, Accurate Barcode Detection in Ultra High-Resolution Images](https://mlsanity.com/paper/2102.06868/) (2021)
- [RetSeg: Retention-based Colorectal Polyps Segmentation Network](https://mlsanity.com/paper/2310.05446/) (2023)
- [Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study](https://mlsanity.com/paper/2101.04034/) (2021)
- [TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection](https://mlsanity.com/paper/2409.19983/) (2024)

## Abstract (arXiv)

Colorectal polyps are important precursors to colon cancer, the third most common cause of cancer mortality for both men and women. It is a disease where early detection is of crucial importance. Colonoscopy is commonly used for early detection of cancer and precancerous pathology. It is a demanding procedure requiring significant amount of time from specialized physicians and nurses, in addition to a significant miss-rates of polyps by specialists. Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way to handle this problem. {However, polyps detection is a challenging problem due to the availability of limited amount of training data and large appearance variations of polyps. To handle this problem, we propose a novel deep learning method Y-Net that consists of two encoder networks with a decoder network. Our proposed Y-Net method} relies on efficient use of pre-trained and un-trained models with novel sum-skip-concatenation operations. Each of the encoders are trained with encoder specific learning rate along the decoder. Compared with the previous methods employing hand-crafted features or 2-D/3-D convolutional neural network, our approach outperforms state-of-the-art methods for polyp detection with 7.3% F1-score and 13% recall improvement.