# A Systematic Review of Few-Shot Learning in Medical Imaging

arXiv: https://arxiv.org/abs/2309.11433 · HTML: https://mlsanity.com/paper/2309.11433/

## 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.

## Builds on (most cited first, 30 of 67)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [How Well Do Self-Supervised Models Transfer?](https://mlsanity.com/paper/2011.13377/) (2020)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Low-Shot Learning from Imaginary Data](https://mlsanity.com/paper/1801.05401/) (2018)
- [Evaluation of Output Embeddings for Fine-Grained Image Classification](https://mlsanity.com/paper/1409.8403/) (2014)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [Learning to Self-Train for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1906.00562/) (2019)
- [Zero-Shot Learning via Semantic Similarity Embedding](https://mlsanity.com/paper/1509.04767/) (2015)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation](https://mlsanity.com/paper/1904.08465/) (2019)

## Built on by (most cited first, 28 of 28)

- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification](https://mlsanity.com/paper/2402.16515/) (2024)
- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2410.01110/) (2024)
- [Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning](https://mlsanity.com/paper/2410.14464/) (2024)
- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging](https://mlsanity.com/paper/2506.18434/) (2025)
- [Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance](https://mlsanity.com/paper/2509.22635/) (2025)
- [Dual Distillation for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2603.01713/) (2026)
- [Foundation AI Model for Medical Image Segmentation](https://mlsanity.com/paper/2411.02745/) (2024)
- [Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification](https://mlsanity.com/paper/2403.17530/) (2024)
- [From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology](https://mlsanity.com/paper/2511.11984/) (2025)
- [Securing Transfer-Learned Networks with Reverse Homomorphic Encryption](https://mlsanity.com/paper/2505.14323/) (2025)
- [Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review](https://mlsanity.com/paper/2505.20306/) (2025)
- [Spatial frequency information fusion network for few-shot learning](https://mlsanity.com/paper/2506.18364/) (2025)
- [An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis](https://mlsanity.com/paper/2507.08050/) (2025)
- [Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling](https://mlsanity.com/paper/2509.13084/) (2025)
- [Automated Procedural Analysis via Video-Language Models for AI-assisted Nursing Skills Assessment](https://mlsanity.com/paper/2509.16810/) (2025)
- [Few-Shot Multimodal Medical Imaging: A Theoretical Framework](https://mlsanity.com/paper/2511.01140/) (2025)
- [Balanced Few-Shot Episodic Learning for Accurate Retinal Disease Diagnosis](https://mlsanity.com/paper/2512.04967/) (2025)
- [PathCo-LatticE: Pathology-Constrained Lattice-Of Experts Framework for Fully-supervised Few-Shot Cardiac MRI Segmentation](https://mlsanity.com/paper/2512.09779/) (2025)
- [VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning](https://mlsanity.com/paper/2604.23641/) (2026)
- [A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction](https://mlsanity.com/paper/2607.20453/) (2026)
- [Active few-shot segmentation by reinforcing data selection](https://mlsanity.com/paper/2607.22371/) (2026)
- [Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation](https://mlsanity.com/paper/2608.05815/) (2026)
- [Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR](https://mlsanity.com/paper/2608.24281/) (2026)
- [Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification](https://mlsanity.com/paper/2609.21541/) (2026)
- [Refining Cytology Predictions with Conditional Random Fields](https://mlsanity.com/paper/2609.31028/) (2026)

## 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.