mlsanityData to 14 Oct 2026

Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

arXiv 2308.06623 ↗First posted 12 Aug 2023eess.IV cs.CVcited by 22

Not on the map. Why?

Abstract (arXiv)

Meta recently released SAM (Segment Anything Model) which is a general-purpose segmentation model. SAM has shown promising results in a wide variety of segmentation tasks including medical image segmentation. In the field of medical image segmentation, polyp segmentation holds a position of high importance, thus creating a model which is robust and precise is quite challenging. Polyp segmentation is a fundamental task to ensure better diagnosis and cure of colorectal cancer. As such in this study, we will see how Polyp-SAM++, a text prompt-aided SAM, can better utilize a SAM using text prompting for robust and more precise polyp segmentation. We will evaluate the performance of a text-guided SAM on the polyp segmentation task on benchmark datasets. We will also compare the results of text-guided SAM vs unprompted SAM. With this study, we hope to advance the field of polyp segmentation and inspire more, intriguing research. The code and other details will be made publically available soon at https://github.com/RisabBiswas/Polyp-SAM++.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 12 Aug 2023.

Builds on

28 of 38 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2023GPT-4 Technical Reportcited by 18,396
  5. 2023Segment Anythingcited by 8,234
  6. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  7. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  8. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  9. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  10. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  11. 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 221
  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. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  3. 2023Can SAM Segment Anything? When SAM Meets Camouflaged Object Detectioncited by 100
  4. 2021Context-aware Cross-level Fusion Network for Camouflaged Object Detectioncited by 86
  5. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  6. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  7. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  8. 2023SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Modelcited by 61
  9. 2023Can SAM Segment Polyps?cited by 59
  10. 2023SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and Morecited by 53
  11. 2023Adaptive Context Selection for Polyp Segmentationcited by 47
  12. 2022Feature Aggregation and Propagation Network for Camouflaged Object Detectioncited by 40
  13. 2023Lesion-aware Dynamic Kernel for Polyp Segmentationcited by 39
  14. 2021Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentationcited by 34
  15. 2023Matting Anythingcited by 31
  16. 2021Duplex Contextual Relation Network for Polyp Segmentationcited by 22

Built on by

Cited by year

Citing papers per year: 2023: 6; 2024: 10; 2025: 6.

22 papers.

Sort:

  1. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  2. 2023Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Promptingcited by 40
  3. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  4. 2023Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomycited by 27
  5. 2024Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentationcited by 25
  6. 2024Biomedical SAM 2: Segment Anything in Biomedical Images and Videoscited by 14
  7. 2024Frontiers in Intelligent Colonoscopycited by 14
  8. 2024Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detectioncited by 8
  9. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  10. 2024Polyp-DAM: Polyp segmentation via depth anything modelcited by 5
  11. 2023DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarizationcited by 4
  12. 2024RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentationcited by 4
10 more
  1. 2024PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentationcited by 3
  2. 2023Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentationcited by 2
  3. 2023A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancementcited by 2
  4. 2025PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamicscited by 2
  5. 2024Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentationcited by 1
  6. 2024LIMIS: Towards Language-based Interactive Medical Image Segmentationcited by 1
  7. 2025AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation Modelscited by 0
  8. 2025BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Modelscited by 0
  9. 2025Prompt Mechanisms in Medical Imaging: A Comprehensive Surveycited by 0
  10. 2025MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentationcited by 0