mlsanityData to 14 Oct 2026

Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation

arXiv 2105.00999 ↗First posted 3 May 2021eess.IV cs.CVcited by 34

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Abstract (arXiv)

Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer. However, polyp segmentation is a challenging task due to the diverse shape, size, color, and texture of polyps, shuttle difference between polyp and its background, as well as low contrast of the colonoscopic images. To address these challenges, we propose a feature enhancement network for accurate polyp segmentation in colonoscopy images. Specifically, the proposed network enhances the semantic information using the novel Semantic Feature Enhance Module (SFEM). Furthermore, instead of directly adding encoder features to the respective decoder layer, we introduce an Adaptive Global Context Module (AGCM), which focuses only on the encoder's significant and hard fine-grained features. The integration of these two modules improves the quality of features layer by layer, which in turn enhances the final feature representation. The proposed approach is evaluated on five colonoscopy datasets and demonstrates superior performance compared to other state-of-the-art models.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

26 of 35 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2016Pyramid Scene Parsing Networkcited by 2,479
  7. 2017Non-local Neural Networkscited by 2,293
  8. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  9. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  10. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  11. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  12. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
14 more
  1. 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 221
  2. 2019A Simple Pooling-Based Design for Real-Time Salient Object Detectioncited by 212
  3. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  4. 2019F3Net: Fusion, Feedback and Focus for Salient Object Detectioncited by 176
  5. 2017Domain-adversarial neural networks to address the appearance variability of histopathology imagescited by 41
  6. 2021Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluationscited by 38
  7. 2018Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Networkcited by 33
  8. 2019Cubic LSTMs for Video Predictioncited by 32
  9. 2013Automated polyp detection in colon capsule endoscopycited by 30
  10. 2020Deep Feature Augmentation for Occluded Image Classificationcited by 21
  11. 2020A Comparative Study on Polyp Classification using Convolutional Neural Networkscited by 12
  12. 2019Adaptively Denoising Proposal Collection for Weakly Supervised Object Localizationcited by 9
  13. 2018An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNNcited by 8
  14. 2020Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filterscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 8; 2024: 12; 2025: 4; 2026: 2.

34 papers.

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  1. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  2. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  3. 2023Can SAM Segment Polyps?cited by 59
  4. 2023Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?cited by 22
  5. 2025BRISC: Annotated Dataset for Brain Tumor Segmentation and Classificationcited by 15
  6. 2023A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trendscited by 12
  7. 2021An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Trainingcited by 10
  8. 2022Aggregating Global Features into Local Vision Transformercited by 10
  9. 2024GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detectioncited by 10
  10. 2024Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detectioncited by 8
  11. 2022XBound-Former: Toward Cross-scale Boundary Modeling in Transformerscited by 6
  12. 2022Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentationcited by 6
22 more
  1. 2024COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detectioncited by 6
  2. 2024Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Surveycited by 5
  3. 2024Polyp-DAM: Polyp segmentation via depth anything modelcited by 5
  4. 2021A Discriminative Channel Diversification Network for Image Classificationcited by 4
  5. 2023Edge-aware Feature Aggregation Network for Polyp Segmentationcited by 4
  6. 2021Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 2
  7. 2021Self-Supervised U-Net for Segmenting Flat and Sessile Polypscited by 2
  8. 2023Gender, Smoking History and Age Prediction from Laryngeal Imagescited by 2
  9. 2023Multi-level feature fusion network combining attention mechanisms for polyp segmentationcited by 2
  10. 2024RTA-Former: Reverse Transformer Attention for Polyp Segmentationcited by 2
  11. 2024Multi-Layer Dense Attention Decoder for Polyp Segmentationcited by 2
  12. 2024PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integrationcited by 2
  13. 2024PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTMcited by 1
  14. 2024Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentationcited by 1
  15. 2025Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robotscited by 1
  16. 2025Adversarial Multi-Task Learning for Liver Tumor Segmentation, Dynamic Enhancement Regression, and Classificationcited by 1
  17. 2026ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentationcited by 1
  18. 2023FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependenciescited by 0
  19. 2024MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with its Application for Colonic Polyp Image Segmentationcited by 0
  20. 2024Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Modelcited by 0
  21. 2025Unlocking Generalization in Polyp Segmentation with DINO Self-Attention "keys"cited by 0
  22. 2026BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentationcited by 0