mlsanityData to 7 Oct 2026

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

arXiv 2103.00429 ↗First posted 28 Feb 2021cs.CV cs.LGcited by 21

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Abstract (arXiv)

Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 205 of its 331 references that are in the map. First posted on arXiv 28 Feb 2021.

Builds on

205 of 331 references.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 4; 2025: 6; 2026: 2.

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  1. 2024Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Modelscited by 20
  2. 2023Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentationcited by 10
  3. 2021Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challengecited by 7
  4. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  5. 2022Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographscited by 5
  6. 2022WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopycited by 5
  7. 2024Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotationcited by 4
  8. 2023Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentationcited by 3
  9. 2022Boundary-aware Information Maximization for Self-supervised Medical Image Segmentationcited by 2
  10. 2023Sparse annotation strategies for segmentation of short axis cardiac MRIcited by 1
  11. 2024Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentationcited by 1
  12. 2023Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRIcited by 0
9 more
  1. 2024Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Losscited by 0
  2. 2025Improving World Models using Deep Supervision with Linear Probescited by 0
  3. 2025TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histologycited by 0
  4. 2025Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentationcited by 0
  5. 2025Out-of-distribution data supervision towards biomedical semantic segmentationcited by 0
  6. 2025Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labelingcited by 0
  7. 2025Leveraging Unlabeled Scans for NCCT Image Segmentation in Early Stroke Diagnosis: A Semi-Supervised GAN Approachcited by 0
  8. 2026Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAMcited by 0
  9. 2026Active few-shot segmentation by reinforcing data selectioncited by 0