# HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

arXiv: https://arxiv.org/abs/2101.07172 · HTML: https://mlsanity.com/paper/2101.07172/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [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)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network](https://mlsanity.com/paper/1802.00368/) (2018)
- [Automated polyp detection in colon capsule endoscopy](https://mlsanity.com/paper/1305.1912/) (2013)
- [Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning](https://mlsanity.com/paper/2011.07631/) (2020)
- [Y-Net: A deep Convolutional Neural Network for Polyp Detection](https://mlsanity.com/paper/1806.01907/) (2018)

## Built on by (most cited first, 30 of 71)

- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2106.06716/) (2021)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [TGANet: Text-guided attention for improved polyp segmentation](https://mlsanity.com/paper/2205.04280/) (2022)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)
- [NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2107.05023/) (2021)
- [FCN-Transformer Feature Fusion for Polyp Segmentation](https://mlsanity.com/paper/2208.08352/) (2022)
- [ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2205.08473/) (2022)
- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing](https://mlsanity.com/paper/2303.07428/) (2023)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2212.11677/) (2022)
- [Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?](https://mlsanity.com/paper/2308.06623/) (2023)
- [TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2206.08985/) (2022)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [BlazeNeo: Blazing fast polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2203.00129/) (2022)
- [Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation](https://mlsanity.com/paper/2206.01741/) (2022)
- [Polyper: Boundary Sensitive Polyp Segmentation](https://mlsanity.com/paper/2312.08735/) (2023)
- [Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection](https://mlsanity.com/paper/2408.05892/) (2024)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2105.00402/) (2021)
- [XBound-Former: Toward Cross-scale Boundary Modeling in Transformers](https://mlsanity.com/paper/2206.00806/) (2022)
- [HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2209.07313/) (2022)
- [DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation](https://mlsanity.com/paper/2210.13595/) (2022)
- [Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation](https://mlsanity.com/paper/2210.15156/) (2022)
- [Meta-Polyp: a baseline for efficient Polyp segmentation](https://mlsanity.com/paper/2305.07848/) (2023)
- [Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model](https://mlsanity.com/paper/2409.09484/) (2024)
- [MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention](https://mlsanity.com/paper/2312.08866/) (2023)

## Abstract (arXiv)

We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a decoder. The backbone is a low memory traffic CNN called HarDNet68, which has been successfully applied to various CV tasks including image classification, object detection, multi-object tracking and semantic segmentation, etc. The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection. We have evaluated HarDNet-MSEG using those five popular datasets. The code and all experiment details are available at Github. https://github.com/james128333/HarDNet-MSEG