mlsanityData to 14 Oct 2026

Attention Mechanisms in Medical Image Segmentation: A Survey

arXiv 2305.17937 ↗First posted 29 May 2023eess.IV cs.CVcited by 9

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Abstract (arXiv)

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 156 of its 332 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

156 of 332 references.

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38 more
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50 most cited of 156 All 156 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 2.

9 papers.

  1. 2025LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Imagescited by 3
  2. 2025Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfacescited by 2
  3. 2025GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentationcited by 2
  4. 2025XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learningcited by 2
  5. 2024Vertical Federated Image Segmentationcited by 0
  6. 2025NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographscited by 0
  7. 2025AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classificationcited by 0
  8. 2026Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)cited by 0
  9. 2026MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentationcited by 0