mlsanityData to 14 Oct 2026

AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation

arXiv 2105.00402 ↗First posted 2 May 2021eess.IV cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps. Colonoscopy is an effective screening tool to detect and remove polyps, especially in the case of precancerous lesions. However, the missing rate in clinical practice is relatively high due to many factors. The procedure could benefit greatly from using AI models for automatic polyp segmentation, which provide valuable insights for improving colon polyp detection. However, precise segmentation is still challenging due to variations of polyps in size, shape, texture, and color. This paper proposes a novel neural network architecture called AG-CUResNeSt, which enhances Coupled UNets using the robust ResNeSt backbone and attention gates. The network is capable of effectively combining multi-level features to yield accurate polyp segmentation. Experimental results on five popular benchmark datasets show that our proposed method achieves state-of-the-art accuracy compared to existing methods.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 2 May 2021.

Builds on

29 of 51 references.

References · 29

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  6. 2017Squeeze-and-Excitation Networkscited by 4,789
  7. 2014Going Deeper with Convolutionscited by 4,241
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  9. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  10. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  11. 2017Visualizing the Loss Landscape of Neural Netscited by 1,130
  12. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
17 more
  1. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  2. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  3. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  4. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  5. 2020ResNeSt: Split-Attention Networkscited by 445
  6. 2019Selective Kernel Networkscited by 403
  7. 2017Road Extraction by Deep Residual U-Netcited by 333
  8. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  9. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  10. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  11. 2020DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentationcited by 125
  12. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  13. 2020DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentationcited by 31
  14. 2019Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approachescited by 16
  15. 2019Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?cited by 13
  16. 2018CU-Net: Coupled U-Netscited by 11
  17. 2020Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNetscited by 4

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 1.

6 papers.

  1. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  2. 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28
  3. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
  4. 2021Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT imagescited by 5
  5. 2022DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Imagescited by 0
  6. 2024Polyp segmentation in colonoscopy images using DeepLabV3++cited by 0