mlsanityData to 7 Oct 2026

Self-supervised learning methods and applications in medical imaging analysis: A survey

arXiv 2109.08685 ↗First posted 17 Sep 2021eess.IV cs.CV cs.LGcited by 76

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Abstract (arXiv)

The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Builds on 80 of its 130 references that are in the map. First posted on arXiv 17 Sep 2021.

Builds on

80 of 130 references.

References · 80

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  6. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  7. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  8. 2016Densely Connected Convolutional Networkscited by 6,691
  9. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  10. 2014Going Deeper with Convolutionscited by 4,231
  11. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  12. 2017Prototypical Networks for Few-shot Learningcited by 3,001
38 more
  1. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  2. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  7. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  8. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  9. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  10. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  11. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  12. 2016Colorful Image Colorizationcited by 1,113
  13. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  14. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,062
  15. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  16. 2020Self-supervised Learning: Generative or Contrastivecited by 693
  17. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 672
  18. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  19. 2020A Survey on Contrastive Self-supervised Learningcited by 553
  20. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  21. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
  22. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  23. 2016Adversarial Feature Learningcited by 350
  24. 2020Keystroke Dynamics as Part of Lifeloggingcited by 334
  25. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  26. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  27. 2021Graph Self-Supervised Learning: A Surveycited by 289
  28. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
  29. 2018cGANs with Projection Discriminatorcited by 253
  30. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  31. 2016Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Predictioncited by 217
  32. 2016Adversarially Learned Inferencecited by 216
  33. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
  34. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  35. 2017Colorization as a Proxy Task for Visual Understandingcited by 175
  36. 2018Learning Latent Permutations with Gumbel-Sinkhorn Networkscited by 172
  37. 2019Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysiscited by 159
  38. 20203D Self-Supervised Methods for Medical Imagingcited by 134

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Citing papers per year: 2022: 13; 2023: 22; 2024: 14; 2025: 19; 2026: 8.

76 papers.

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  1. 2023RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarioscited by 46
  2. 2022WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinomacited by 32
  3. 2024Review of multimodal machine learning approaches in healthcarecited by 30
  4. 2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
  5. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  6. 2023From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentationcited by 20
  7. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  8. 2023LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speechcited by 17
  9. 2025A Survey of Pathology Foundation Model: Progress and Future Directionscited by 14
  10. 2022Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationcited by 13
  11. 2022Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learnerscited by 11
  12. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
38 more
  1. 2022SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verificationcited by 8
  2. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  3. 2023CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversioncited by 6
  4. 2025Your contrastive learning problem is secretly a distribution alignment problemcited by 6
  5. 2022IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentationcited by 5
  6. 2024Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyondcited by 5
  7. 2025Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Imagescited by 5
  8. 2025A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastomacited by 4
  9. 2023Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problemcited by 3
  10. 2024OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysiscited by 3
  11. 2024Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learningcited by 3
  12. 2025Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directionscited by 3
  13. 2026TopoCL: Topological Contrastive Learning for Medical Imagingcited by 3
  14. 2022Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Trainingcited by 2
  15. 2023Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysiscited by 2
  16. 2023Unleash Model Potential: Bootstrapped Meta Self-supervised Learningcited by 2
  17. 2023A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Imagescited by 2
  18. 2024A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal Endoscopycited by 2
  19. 2024Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-trainingcited by 2
  20. 2024Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervisioncited by 2
  21. 2022RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Imagescited by 1
  22. 2022Analysing the effectiveness of a generative model for semi-supervised medical image segmentationcited by 1
  23. 2023DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Imagescited by 1
  24. 2023Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpoxcited by 1
  25. 2023Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representationscited by 1
  26. 2023M(otion)-mode Based Prediction of Ejection Fraction using Echocardiogramscited by 1
  27. 2024Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformercited by 1
  28. 2024BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learningcited by 1
  29. 2025Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomographycited by 1
  30. 2025MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencodercited by 1
  31. 2025C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomycited by 1
  32. 2025Physics-Informed Machine Learning for Efficient Sim-to-Real Data Augmentation in Micro-Object Pose Estimationcited by 1
  33. 2022Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Imagescited by 0
  34. 2022Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Taskscited by 0
  35. 2022Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizercited by 0
  36. 2022A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRIcited by 0
  37. 2023An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysiscited by 0
  38. 2023DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillationcited by 0

50 most cited of 76 All 76 (JSON)