mlsanityData to 7 Oct 2026

Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

arXiv 2108.11154 ↗First posted 25 Aug 2021cs.CVcited by 14

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Abstract (arXiv)

Segmentation of images is a long-standing challenge in medical AI. This is mainly due to the fact that training a neural network to perform image segmentation requires a significant number of pixel-level annotated data, which is often unavailable. To address this issue, we propose a semi-supervised image segmentation technique based on the concept of multi-view learning. In contrast to the previous art, we introduce an adversarial form of dual-view training and employ a critic to formulate the learning problem in multi-view training as a min-max problem. Thorough quantitative and qualitative evaluations on several datasets indicate that our proposed method outperforms state-of-the-art medical image segmentation algorithms consistently and comfortably. The code is publicly available at https://github.com/himashi92/Duo-SegNet

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

11 of 19 references.

References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  3. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  4. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  5. 2021The Medical Segmentation Decathloncited by 620
  6. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  7. 2013A Survey on Multi-view Learningcited by 152
  8. 2018Deep Co-Training for Semi-Supervised Image Recognitioncited by 145
  9. 2019Deep Co-Training for Semi-Supervised Image Segmentationcited by 58
  10. 2018Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Imagescited by 55
  11. 2018Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentationcited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

14 papers.

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  1. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  2. 2022Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentationcited by 28
  3. 2023SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentationcited by 12
  4. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  5. 2023Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentationcited by 6
  6. 2023GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentationcited by 4
  7. 2022Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Trainingcited by 2
  8. 2022Analysing the effectiveness of a generative model for semi-supervised medical image segmentationcited by 1
  9. 2023Semi-Supervised Medical Image Segmentation with Co-Distribution Alignmentcited by 1
  10. 2024Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentationcited by 1
  11. 2024Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Viewscited by 0
  12. 2025Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Surveycited by 0
2 more
  1. 2025Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentationcited by 0
  2. 2026SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentationcited by 0