mlsanityData to 7 Oct 2026

Kvasir-SEG: A Segmented Polyp Dataset

arXiv 1911.07069 ↗First posted 16 Nov 2019eess.IV cs.CVcited by 588

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Cited by 25 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

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.

Key facts. Cited by 588 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2019.

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Citing papers per year: 2021: 36; 2022: 51; 2023: 93; 2024: 135; 2025: 147; 2026: 126.

588 papers.

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  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  2. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  5. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  6. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 109
  7. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  8. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  9. 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Groupingcited by 92
  10. 2022Video Polyp Segmentation: A Deep Learning Perspectivecited by 81
  11. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  12. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
38 more
  1. 2021Progressively Normalized Self-Attention Network for Video Polyp Segmentationcited by 75
  2. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  3. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  4. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  5. 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Modelcited by 67
  6. 2022Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Studycited by 66
  7. 2022Deep Gradient Learning for Efficient Camouflaged Object Detectioncited by 65
  8. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  9. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  10. 2021A multi-centre polyp detection and segmentation dataset for generalisability assessmentcited by 63
  11. 2024SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentationcited by 63
  12. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  13. 2021UACANet: Uncertainty Augmented Context Attention for Polyp Segmentationcited by 61
  14. 2022TGANet: Text-guided attention for improved polyp segmentationcited by 59
  15. 2023Can SAM Segment Polyps?cited by 59
  16. 2024SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and Morecited by 58
  17. 2022Stepwise Feature Fusion: Local Guides Globalcited by 56
  18. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  19. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  20. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  21. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  22. 2022EndoMapper dataset of complete calibrated endoscopy procedurescited by 53
  23. 2023SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and Morecited by 53
  24. 2024Vivim: a Video Vision Mamba for Medical Video Segmentationcited by 51
  25. 2021NeoUNet: Towards accurate colon polyp segmentation and neoplasm detectioncited by 50
  26. 2023Adaptive Context Selection for Polyp Segmentationcited by 47
  27. 2023Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasetscited by 45
  28. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
  29. 2021Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Networkcited by 42
  30. 2021A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentationcited by 41
  31. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  32. 2022Feature Aggregation and Propagation Network for Camouflaged Object Detectioncited by 40
  33. 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
  34. 2023Gradient-based Parameter Selection for Efficient Fine-Tuningcited by 40
  35. 2023Lesion-aware Dynamic Kernel for Polyp Segmentationcited by 39
  36. 2023Segment Anything Model for Medical Images?cited by 39
  37. 2021Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluationscited by 38
  38. 2022FCN-Transformer Feature Fusion for Polyp Segmentationcited by 37

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