mlsanityData to 14 Oct 2026

MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy

arXiv 2412.19464 ↗First posted 27 Dec 2024cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in preserving precise polyp boundaries, incorporating multi-scale features, and modeling spatial dependencies that accurately reflect the intricate and diverse morphology of polyps. Methods: To address these limitations, we propose a novel Multiscale Network with Spatial-enhanced Attention (MNet-SAt) for polyp segmentation in colonoscopy images. This framework incorporates four key modules: Edge-Guided Feature Enrichment (EGFE) preserves edge information for improved boundary quality; Multi-Scale Feature Aggregator (MSFA) extracts and aggregates multi-scale features across channel spatial dimensions, focusing on salient regions; Spatial-Enhanced Attention (SEAt) captures spatial-aware global dependencies within the multi-scale aggregated features, emphasizing the region of interest; and Channel-Enhanced Atrous Spatial Pyramid Pooling (CE-ASPP) resamples and recalibrates attentive features across scales. Results: We evaluated MNet-SAt on the Kvasir-SEG and CVC-ClinicDB datasets, achieving Dice Similarity Coefficients of 96.61% and 98.60%, respectively. Conclusion: Both quantitative (DSC) and qualitative assessments highlight MNet-SAt's superior performance and generalization capabilities compared to existing methods. Significance: MNet-SAt's high accuracy in polyp segmentation holds promise for improving clinical workflows in early polyp detection and more effective treatment, contributing to reduced colorectal cancer mortality rates.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 27 Dec 2024.

Builds on

22 of 49 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  4. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  5. 2018Group Normalizationcited by 1,524
  6. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  7. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  8. 2020Image Segmentation Using Deep Learning: A Surveycited by 660
  9. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  10. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  11. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
  12. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
10 more
  1. 2022TGANet: Text-guided attention for improved polyp segmentationcited by 59
  2. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  3. 2022FCN-Transformer Feature Fusion for Polyp Segmentationcited by 37
  4. 2023Using DUCK-Net for Polyp Image Segmentationcited by 31
  5. 2019Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Imagescited by 28
  6. 2019FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentationcited by 24
  7. 2024A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and methodcited by 12
  8. 2024CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detectioncited by 7
  9. 2024An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detectioncited by 6
  10. 2023Multi-level feature fusion network combining attention mechanisms for polyp segmentationcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directionscited by 6
  2. 2025An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Citiescited by 2
  3. 2025T-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentationcited by 1
  4. 2025A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield predictioncited by 1
  5. 2025Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Changecited by 0