mlsanityData to 14 Oct 2026

Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

arXiv 2305.04401 ↗First posted 8 May 2023eess.IV cs.CVcited by 9

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Abstract (arXiv)

Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy application of deep learning in the medical sector. To unravel the confined data source, researchers have developed a model that can solve machine learning problems with fewer data called ``Few shot learning". Few hot learning algorithms determine to solve the data limitation problems by extracting the characteristics from a small dataset through classification and segmentation methods. In the medical sector, there is frequently a shortage of available datasets in respect of some confidential diseases. Therefore, Few shot learning gets the limelight in this data scarcity sector. In this chapter, the background and basic overview of a few shots of learning is represented. Henceforth, the classification of few-shot learning is described also. Even the paper shows a comparison of methodological approaches that are applied in medical image analysis over time. The current advancement in the implementation of few-shot learning concerning medical imaging is illustrated. The future scope of this domain in the medical imaging sector is further described.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 29 of its 61 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

29 of 61 references.

References · 29

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  5. 2019A Closer Look at Few-shot Classificationcited by 684
  6. 2017Deep Learning for Medical Image Analysiscited by 401
  7. 2019A Baseline for Few-Shot Image Classificationcited by 298
  8. 2017Meta Networkscited by 269
  9. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  10. 2020Interventional Few-Shot Learningcited by 112
  11. 2017Deep Learning for Medical Image Processing: Overview, Challenges and Futurecited by 104
  12. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
17 more
  1. 2020The Unreasonable Effectiveness of Deep Learning in Artificial Intelligencecited by 57
  2. 2020Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embeddingcited by 48
  3. 2018Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learningcited by 36
  4. 2019Automatic detection of rare pathologies in fundus photographs using few-shot learningcited by 20
  5. 2018Few-shot Learning for Named Entity Recognition in Medical Textcited by 19
  6. 2020Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphscited by 19
  7. 2020Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imagingcited by 18
  8. 2021Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Reviewcited by 18
  9. 2021Meta-learning with implicit gradients in a few-shot setting for medical image segmentationcited by 15
  10. 2022Few-shot learning for medical text: A systematic reviewcited by 13
  11. 2018AffinityNet: semi-supervised few-shot learning for disease type predictioncited by 12
  12. 2020Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentationcited by 12
  13. 2019Few-Shot Learning-Based Human Activity Recognitioncited by 11
  14. 2017Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrievalcited by 6
  15. 2020Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Imagescited by 6
  16. 2019Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognitioncited by 5
  17. 2017High-dimensional brain. A tool for encoding and rapid learning of memories by single neuronscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.

9 papers.

  1. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  2. 2023GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Datasetcited by 7
  3. 2024Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Studycited by 3
  4. 2024PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classificationcited by 3
  5. 2026RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generationcited by 1
  6. 2024Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learningcited by 0
  7. 2025Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learningcited by 0
  8. 2025Few-Shot Multimodal Medical Imaging: A Theoretical Frameworkcited by 0
  9. 2026Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasetscited by 0