# Meta-Polyp: a baseline for efficient Polyp segmentation

arXiv: https://arxiv.org/abs/2305.07848 · HTML: https://mlsanity.com/paper/2305.07848/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [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)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [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)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [MetaFormer Baselines for Vision](https://mlsanity.com/paper/2210.13452/) (2022)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice](https://mlsanity.com/paper/1911.01685/) (2019)
- [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)
- [Lesion-aware Dynamic Kernel for Polyp Segmentation](https://mlsanity.com/paper/2301.04904/) (2023)
- [Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations](https://mlsanity.com/paper/1906.11031/) (2019)
- [Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation](https://mlsanity.com/paper/2012.15244/) (2020)
- [FCB-SwinV2 Transformer for Polyp Segmentation](https://mlsanity.com/paper/2302.01027/) (2023)
- [Refined Deep Neural Network and U-Net for Polyps Segmentation](https://mlsanity.com/paper/2105.14848/) (2021)

## Built on by (most cited first, 6 of 6)

- [SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation](https://mlsanity.com/paper/2406.14819/) (2024)
- [RetSeg: Retention-based Colorectal Polyps Segmentation Network](https://mlsanity.com/paper/2310.05446/) (2023)
- [Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation](https://mlsanity.com/paper/2510.20933/) (2025)
- [KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation](https://mlsanity.com/paper/2312.08555/) (2023)
- [Polyp segmentation in colonoscopy images using DeepLabV3++](https://mlsanity.com/paper/2407.19327/) (2024)
- [PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block](https://mlsanity.com/paper/2509.23751/) (2025)

## Abstract (arXiv)

In recent years, polyp segmentation has gained significant importance, and many methods have been developed using CNN, Vision Transformer, and Transformer techniques to achieve competitive results. However, these methods often face difficulties when dealing with out-of-distribution datasets, missing boundaries, and small polyps. In 2022, Meta-Former was introduced as a new baseline for vision, which not only improved the performance of multi-task computer vision but also addressed the limitations of the Vision Transformer and CNN family backbones. To further enhance segmentation, we propose a fusion of Meta-Former with UNet, along with the introduction of a Multi-scale Upsampling block with a level-up combination in the decoder stage to enhance the texture, also we propose the Convformer block base on the idea of the Meta-former to enhance the crucial information of the local feature. These blocks enable the combination of global information, such as the overall shape of the polyp, with local information and boundary information, which is crucial for the decision of the medical segmentation. Our proposed approach achieved competitive performance and obtained the top result in the State of the Art on the CVC-300 dataset, Kvasir, and CVC-ColonDB dataset. Apart from Kvasir-SEG, others are out-of-distribution datasets. The implementation can be found at: https://github.com/huyquoctrinh/MetaPolyp-CBMS2023.