mlsanityData to 7 Oct 2026

Attention U-Net: Learning Where to Look for the Pancreas

arXiv 1804.03999 ↗First posted 11 Apr 2018cs.CVcited by 1,420

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Cited by 54 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.

Key facts. Cited by 1,420 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2018.

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Citing papers per year: 2021: 175; 2022: 190; 2023: 255; 2024: 280; 2025: 304; 2026: 216.

1,420 papers.

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  1. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  2. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  3. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  4. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  5. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  6. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
  7. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 277
  8. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  9. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  10. 2022Transformers in Medical Imaging: A Surveycited by 245
  11. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  12. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
38 more
  1. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  2. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Modelscited by 191
  3. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  4. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  5. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  6. 2021Walk in the Cloud: Learning Curves for Point Clouds Shape Analysiscited by 172
  7. 2021Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videoscited by 139
  8. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentationcited by 133
  9. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  10. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  11. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  12. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 110
  13. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  14. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  15. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  16. 2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 92
  17. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  18. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  19. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  20. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  21. 2023High-fidelity 3D Human Digitization from Single 2K Resolution Imagescited by 70
  22. 2022A General Survey on Attention Mechanisms in Deep Learningcited by 64
  23. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  24. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  25. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  26. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  27. 2021UACANet: Uncertainty Augmented Context Attention for Polyp Segmentationcited by 61
  28. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  29. 2021FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysiscited by 60
  30. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  31. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  32. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
  33. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  34. 2022ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Imagescited by 54
  35. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  36. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  37. 2021RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Userscited by 52
  38. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Modelcited by 52

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