# Kvasir-SEG: A Segmented Polyp Dataset

arXiv: https://arxiv.org/abs/1911.07069 · HTML: https://mlsanity.com/paper/1911.07069/

## Key facts

- Cited by 589 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2019.

## Built on by (most cited first, 30 of 589)

- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [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)
- [Foundational Models Defining a New Era in Vision: A Survey and Outlook](https://mlsanity.com/paper/2307.13721/) (2023)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping](https://mlsanity.com/paper/2305.11003/) (2023)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [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)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model](https://mlsanity.com/paper/2401.17868/) (2024)
- [Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study](https://mlsanity.com/paper/2209.15517/) (2022)
- [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022)
- [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023)
- [EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2405.06880/) (2024)
- [SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation](https://mlsanity.com/paper/2408.08870/) (2024)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation](https://mlsanity.com/paper/2107.02368/) (2021)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [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)
- [Stepwise Feature Fusion: Local Guides Global](https://mlsanity.com/paper/2203.03635/) (2022)
- [Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model](https://mlsanity.com/paper/2304.11332/) (2023)

## Abstract (arXiv)

Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist. Moreover, we also generated the bounding boxes of the polyp regions with the help of segmentation masks. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network (CNN) approach. The dataset will be of value for researchers to reproduce results and compare methods. By adding segmentation masks to the Kvasir dataset, which only provide frame-wise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy images.