mlsanityData to 14 Oct 2026

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

arXiv 1612.00799 ↗First posted 2 Dec 2016cs.CVcited by 221

Not on the map. Why?

Abstract (arXiv)

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.

Key facts. Cited by 221 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Dec 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 26; 2023: 42; 2024: 52; 2025: 47; 2026: 32.

221 papers.

Sort:

By area:

  1. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  2. 2022Transformers in Medical Imaging: A Surveycited by 245
  3. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  4. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  5. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  6. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  7. 2022Video Polyp Segmentation: A Deep Learning Perspectivecited by 82
  8. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 82
  9. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  10. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  11. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  12. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
38 more
  1. 2024Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Modelcited by 69
  2. 2022Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Studycited by 66
  3. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  4. 2024SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentationcited by 64
  5. 2021UACANet: Uncertainty Augmented Context Attention for Polyp Segmentationcited by 61
  6. 2023Can SAM Segment Polyps?cited by 59
  7. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  8. 2023Adaptive Context Selection for Polyp Segmentationcited by 47
  9. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
  10. 2021Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Networkcited by 42
  11. 2021A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentationcited by 41
  12. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  13. 2023HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidancecited by 36
  14. 2021Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentationcited by 34
  15. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 33
  16. 2022Camouflaged Object Detection via Context-aware Cross-level Fusioncited by 32
  17. 2023ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Modelscited by 31
  18. 2023Using DUCK-Net for Polyp Image Segmentationcited by 31
  19. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 29
  20. 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28
  21. 2023Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Modelscited by 28
  22. 2023MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentationcited by 28
  23. 2024Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attentioncited by 26
  24. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  25. 2023U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentationcited by 23
  26. 2024Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentationcited by 23
  27. 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentationcited by 23
  28. 2021Duplex Contextual Relation Network for Polyp Segmentationcited by 22
  29. 2022DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentationcited by 22
  30. 2023Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?cited by 22
  31. 2023G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentationcited by 21
  32. 2021Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopycited by 20
  33. 2021Membership Inference Attacks are Easier on Difficult Problemscited by 17
  34. 2023Towards Diverse Binary Segmentation via A Simple yet General Gated Networkcited by 17
  35. 2023Transformers in Healthcare: A Surveycited by 17
  36. 2024Localized Adaptive Risk Controlcited by 17
  37. 2024Automatically Adaptive Conformal Risk Controlcited by 16
  38. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15

50 most cited of 221 All 221 (JSON)