# Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

arXiv: https://arxiv.org/abs/2105.07467 · HTML: https://mlsanity.com/paper/2105.07467/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 74 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 30 of 32)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://mlsanity.com/paper/1709.04696/) (2017)
- [Global Second-order Pooling Convolutional Networks](https://mlsanity.com/paper/1811.12006/) (2018)
- [MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction](https://mlsanity.com/paper/2007.13559/) (2020)
- [Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network](https://mlsanity.com/paper/1802.00368/) (2018)
- [USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets](https://mlsanity.com/paper/1904.08254/) (2019)
- [MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning](https://mlsanity.com/paper/1910.09308/) (2019)

## Built on by (most cited first, 20 of 20)

- [Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention](https://mlsanity.com/paper/2405.06284/) (2024)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps](https://mlsanity.com/paper/2205.15413/) (2022)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [Rethinking Polyp Segmentation from an Out-of-Distribution Perspective](https://mlsanity.com/paper/2306.07792/) (2023)
- [MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer](https://mlsanity.com/paper/2401.12439/) (2024)
- [Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training](https://mlsanity.com/paper/2202.06104/) (2022)
- [Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data](https://mlsanity.com/paper/2307.12033/) (2023)
- [PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM](https://mlsanity.com/paper/2402.11585/) (2024)
- [Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation](https://mlsanity.com/paper/2405.12328/) (2024)
- [A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n](https://mlsanity.com/paper/2507.10864/) (2025)
- [Focal Attention Networks: optimising attention for biomedical image segmentation](https://mlsanity.com/paper/2111.00534/) (2021)
- [DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images](https://mlsanity.com/paper/2211.14235/) (2022)
- [Optimized Hybrid Focal Margin Loss for Crack Segmentation](https://mlsanity.com/paper/2302.04395/) (2023)
- [Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task](https://mlsanity.com/paper/2309.06807/) (2023)
- [Polyp segmentation in colonoscopy images using DeepLabV3++](https://mlsanity.com/paper/2407.19327/) (2024)
- [Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications](https://mlsanity.com/paper/2411.02843/) (2024)
- [Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps](https://mlsanity.com/paper/2412.02443/) (2024)
- [Unlocking Generalization in Polyp Segmentation with DINO Self-Attention "keys"](https://mlsanity.com/paper/2512.13376/) (2025)
- [LUMOS: Latent Universal Medical Priors for Segmentation](https://mlsanity.com/paper/2603.01115/) (2026)

## Abstract (arXiv)

Background: Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. Method: In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net further incorporates short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics. Results: Our model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14% and 15% improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively. Conclusions: This study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive screening and more broadly to other biomedical image segmentation tasks involving class imbalance and requiring efficiency.