mlsanityData to 7 Oct 2026

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

arXiv 1807.10165 ↗First posted 18 Jul 2018cs.CV cs.LG eess.IVcited by 1,518

Not on the map. Why?

Cited by 45 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.

Key facts. Cited by 1,518 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 185; 2022: 236; 2023: 274; 2024: 286; 2025: 310; 2026: 227.

1,518 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. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  4. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  5. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  6. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  7. 2021Remote Sensing Image Change Detection with Transformerscited by 305
  8. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
  9. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  10. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  11. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  12. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
38 more
  1. 2021Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalizationcited by 248
  2. 2021Exploring Cross-Image Pixel Contrast for Semantic Segmentationcited by 242
  3. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  4. 2021Concealed Object Detectioncited by 212
  5. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  6. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  7. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  8. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  9. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 166
  10. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  11. 2021Camouflaged Object Segmentation with Distraction Miningcited by 151
  12. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
  13. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  14. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  15. 2023Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentationcited by 115
  16. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  17. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 110
  18. 2021RFN-Nest: An end-to-end residual fusion network for infrared and visible imagescited by 109
  19. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  20. 2022SegViT: Semantic Segmentation with Plain Vision Transformerscited by 105
  21. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  22. 2021Mutual Graph Learning for Camouflaged Object Detectioncited by 103
  23. 2021MISSFormer: An Effective Medical Image Segmentation Transformercited by 103
  24. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  25. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 94
  26. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  27. 2023Ambiguous Medical Image Segmentation using Diffusion Modelscited by 92
  28. 2021Context-aware Cross-level Fusion Network for Camouflaged Object Detectioncited by 86
  29. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  30. 2024FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenescited by 86
  31. 2022SimpleRecon: 3D Reconstruction Without 3D Convolutionscited by 83
  32. 2021U-Net Transformer: Self and Cross Attention for Medical Image Segmentationcited by 81
  33. 2022Video Polyp Segmentation: A Deep Learning Perspectivecited by 81
  34. 2022High-resolution Iterative Feedback Network for Camouflaged Object Detectioncited by 78
  35. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  36. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  37. 2021Progressively Normalized Self-Attention Network for Video Polyp Segmentationcited by 75
  38. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73

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