mlsanityData to 14 Oct 2026

A Systematic Review of Few-Shot Learning in Medical Imaging

arXiv 2309.11433 ↗First posted 20 Sep 2023cs.CV cs.AIcited by 28

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Abstract (arXiv)

The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning. This systematic review gives a comprehensive overview of few-shot learning in medical imaging. We searched the literature systematically and selected 80 relevant articles published from 2018 to 2023. We clustered the articles based on medical outcomes, such as tumour segmentation, disease classification, and image registration; anatomical structure investigated (i.e. heart, lung, etc.); and the meta-learning method used. For each cluster, we examined the papers' distributions and the results provided by the state-of-the-art. In addition, we identified a generic pipeline shared among all the studies. The review shows that few-shot learning can overcome data scarcity in most outcomes and that meta-learning is a popular choice to perform few-shot learning because it can adapt to new tasks with few labelled samples. In addition, following meta-learning, supervised learning and semi-supervised learning stand out as the predominant techniques employed to tackle few-shot learning challenges in medical imaging and also best performing. Lastly, we observed that the primary application areas predominantly encompass cardiac, pulmonary, and abdominal domains. This systematic review aims to inspire further research to improve medical image analysis and patient care.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 67 of its 140 references that are in the map. First posted on arXiv 20 Sep 2023.

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67 of 140 references.

References · 67

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2016Matching Networks for One Shot Learningcited by 2,255
  5. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  6. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  7. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  8. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  9. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  10. 2019A Closer Look at Few-shot Classificationcited by 684
  11. 2014Neural Turing Machinescited by 644
  12. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
38 more
  1. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  2. 2014Deep metric learning using Triplet networkcited by 422
  3. 2017A simple neural network module for relational reasoningcited by 346
  4. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  5. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 255
  6. 2015Semi-Supervised Learning with Ladder Networkscited by 247
  7. 2020How Well Do Self-Supervised Models Transfer?cited by 213
  8. 2016Learning to Optimizecited by 195
  9. 2018Low-Shot Learning from Imaginary Datacited by 181
  10. 2014Evaluation of Output Embeddings for Fine-Grained Image Classificationcited by 166
  11. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  12. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  13. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  14. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  15. 2019Learning to Self-Train for Semi-Supervised Few-Shot Classificationcited by 83
  16. 2015Zero-Shot Learning via Semantic Similarity Embeddingcited by 74
  17. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  18. 2019DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentationcited by 66
  19. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  20. 2020Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embeddingcited by 48
  21. 2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
  22. 2020LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentationcited by 28
  23. 2019One Shot Learning for Deformable Medical Image Registration and Periodic Motion Trackingcited by 21
  24. 2020Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Reviewcited by 20
  25. 2021Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentationcited by 20
  26. 2020Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imagingcited by 18
  27. 2020Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Imagescited by 17
  28. 2021Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentationcited by 17
  29. 2021A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentationcited by 17
  30. 2021A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Datacited by 16
  31. 2021Meta-learning with implicit gradients in a few-shot setting for medical image segmentationcited by 15
  32. 2021Few-Shot Domain Adaptation with Polymorphic Transformerscited by 14
  33. 2020GroupRegNet: A Groupwise One-shot Deep Learning-based 4D Image Registration Methodcited by 12
  34. 2020Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentationcited by 12
  35. 2022CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentationcited by 11
  36. 2021Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentationcited by 10
  37. 2020Anatomical Data Augmentation via Fluid-based Image Registrationcited by 9
  38. 2021Weakly Supervised Few-Shot Segmentation Via Meta-Learningcited by 9

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Citing papers per year: 2023: 1; 2024: 6; 2025: 13; 2026: 8.

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  1. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 23
  2. 2024LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classificationcited by 8
  3. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  4. 2024RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentationcited by 6
  5. 2024Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learningcited by 5
  6. 2025Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2cited by 5
  7. 2025Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imagingcited by 4
  8. 2025Training-Free Synthetic Data Generation with Dual IP-Adapter Guidancecited by 3
  9. 2026Dual Distillation for Few-Shot Anomaly Detectioncited by 3
  10. 2024Foundation AI Model for Medical Image Segmentationcited by 2
  11. 2024Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classificationcited by 1
  12. 2025From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathologycited by 1
16 more
  1. 2025Securing Transfer-Learned Networks with Reverse Homomorphic Encryptioncited by 0
  2. 2025Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Reviewcited by 0
  3. 2025Spatial frequency information fusion network for few-shot learningcited by 0
  4. 2025An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosiscited by 0
  5. 2025Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labelingcited by 0
  6. 2025Automated Procedural Analysis via Video-Language Models for AI-assisted Nursing Skills Assessmentcited by 0
  7. 2025Few-Shot Multimodal Medical Imaging: A Theoretical Frameworkcited by 0
  8. 2025Balanced Few-Shot Episodic Learning for Accurate Retinal Disease Diagnosiscited by 0
  9. 2025PathCo-LatticE: Pathology-Constrained Lattice-Of Experts Framework for Fully-supervised Few-Shot Cardiac MRI Segmentationcited by 0
  10. 2026VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learningcited by 0
  11. 2026A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Predictioncited by 0
  12. 2026Active few-shot segmentation by reinforcing data selectioncited by 0
  13. 2026Bayesian adaptively-weighted ensembles for few-shot abdominal segmentationcited by 0
  14. 2026Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETRcited by 0
  15. 2026Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classificationcited by 0
  16. 2026Refining Cytology Predictions with Conditional Random Fieldscited by 0