mlsanityData to 7 Oct 2026

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

arXiv 2006.11392 ↗First posted 13 Jun 2020eess.IV cs.CVcited by 460

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Abstract (arXiv)

Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating any misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency.

Key facts. Cited by 460 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2020.

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Citing papers per year: 2021: 52; 2022: 60; 2023: 92; 2024: 107; 2025: 88; 2026: 61.

460 papers.

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  6. 2023Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformerscited by 87
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  9. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
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  11. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  12. 2021Progressively Normalized Self-Attention Network for Video Polyp Segmentationcited by 75
  13. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  14. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
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  19. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  20. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  21. 2024SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentationcited by 63
  22. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  23. 2021UACANet: Uncertainty Augmented Context Attention for Polyp Segmentationcited by 61
  24. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  25. 2022Implicit Motion Handling for Video Camouflaged Object Detectioncited by 60
  26. 2022TGANet: Text-guided attention for improved polyp segmentationcited by 59
  27. 2023Can SAM Segment Polyps?cited by 59
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  29. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
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  33. 2024Vivim: a Video Vision Mamba for Medical Video Segmentationcited by 51
  34. 2021NeoUNet: Towards accurate colon polyp segmentation and neoplasm detectioncited by 50
  35. 2023ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detectioncited by 48
  36. 2021CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detectioncited by 47
  37. 2024Frequency-Spatial Entanglement Learning for Camouflaged Object Detectioncited by 46
  38. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45

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