mlsanityData to 7 Oct 2026

Can SAM Segment Polyps?

arXiv 2304.07583 ↗First posted 15 Apr 2023cs.CVcited by 59

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Abstract (arXiv)

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 15 Apr 2023.

Builds on

28 of 38 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2023Segment Anythingcited by 8,158
  6. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  7. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
  8. 2017Structure-measure: A New Way to Evaluate Foreground Mapscited by 480
  9. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  10. 2018Enhanced-alignment Measure for Binary Foreground Map Evaluationcited by 370
  11. 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 220
  12. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
16 more
  1. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  2. 2023Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applicationscited by 121
  3. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  4. 2023Can SAM Segment Anything? When SAM Meets Camouflaged Object Detectioncited by 99
  5. 2021Context-aware Cross-level Fusion Network for Camouflaged Object Detectioncited by 86
  6. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  7. 2023SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anythingcited by 75
  8. 2020RGB-D Salient Object Detection: A Surveycited by 73
  9. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  10. 2021Specificity-preserving RGB-D Saliency Detectioncited by 70
  11. 2023SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Modelcited by 61
  12. 2023Adaptive Context Selection for Polyp Segmentationcited by 47
  13. 2022Feature Aggregation and Propagation Network for Camouflaged Object Detectioncited by 40
  14. 2023Lesion-aware Dynamic Kernel for Polyp Segmentationcited by 39
  15. 2021Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentationcited by 33
  16. 2021Duplex Contextual Relation Network for Polyp Segmentationcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 33; 2024: 21; 2025: 3; 2026: 2.

59 papers.

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  1. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  2. 2023Segment Anything in Medical Imagescited by 226
  3. 2023SAM-Med2Dcited by 123
  4. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 101
  5. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  6. 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Groupingcited by 92
  7. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  8. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  9. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
  10. 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Modelcited by 67
  11. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  12. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
38 more
  1. 2023Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasetscited by 45
  2. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  3. 2023Are Natural Domain Foundation Models Useful for Medical Image Classification?cited by 44
  4. 2023SAM3D: Zero-Shot 3D Object Detection via Segment Anything Modelcited by 40
  5. 2023Segment Anything Model for Medical Images?cited by 39
  6. 2023Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentationcited by 35
  7. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  8. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  9. 2024Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentationcited by 25
  10. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  11. 2023RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentationcited by 24
  12. 2023An Empirical Study on the Robustness of the Segment Anything Model (SAM)cited by 23
  13. 2023Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?cited by 22
  14. 2023Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Surveycited by 21
  15. 2023Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learningcited by 18
  16. 2024SAFIRE: Segment Any Forged Image Regioncited by 18
  17. 2024Interactive 3D Medical Image Segmentation with SAM 2cited by 17
  18. 2023mathrmSAMMed: A medical image annotation framework based on large vision modelcited by 13
  19. 2023Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentationcited by 12
  20. 2023SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentationcited by 12
  21. 2023A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trendscited by 12
  22. 2024ASPS: Augmented Segment Anything Model for Polyp Segmentationcited by 12
  23. 2025Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptationcited by 12
  24. 2023Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentationcited by 9
  25. 2024Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detectioncited by 8
  26. 2024SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentationcited by 7
  27. 2024Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?cited by 6
  28. 2024Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Surveycited by 5
  29. 2024Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Networkcited by 5
  30. 2023Rethinking Polyp Segmentation from an Out-of-Distribution Perspectivecited by 4
  31. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  32. 2024MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentationcited by 4
  33. 2023Empirical Analysis of a Segmentation Foundation Model in Prostate Imagingcited by 3
  34. 2023CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasetscited by 3
  35. 2024ProMISe: Promptable Medical Image Segmentation using SAMcited by 3
  36. 2026Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarioscited by 3
  37. 2023Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentationcited by 2
  38. 2024Foundation AI Model for Medical Image Segmentationcited by 2

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