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A Survey on Deep Learning in Medical Image Analysis

arXiv 1702.05747 ↗First posted 19 Feb 2017cs.CVcited by 1,182

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Cited by 23 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.

Key facts. Cited by 1,182 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 244; 2022: 172; 2023: 164; 2024: 177; 2025: 232; 2026: 193.

1,182 papers.

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  1. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  2. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  3. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  4. 2022Transformers in Medical Imaging: A Surveycited by 245
  5. 2022Towards Multi-spatiotemporal-scale Generalized PDE Modelingcited by 190
  6. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  7. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
  8. 2021Explainable artificial intelligence (XAI) in deep learning-based medical image analysiscited by 147
  9. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  10. 2022SimpleClick: Interactive Image Segmentation with Simple Vision Transformerscited by 109
  11. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  12. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
38 more
  1. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  2. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  3. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
  4. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
  5. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  6. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  7. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  8. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  9. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 76
  10. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  11. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  12. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  13. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  14. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  15. 2021FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysiscited by 60
  16. 2023A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Futurecited by 59
  17. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  18. 2022Towards a Guideline for Evaluation Metrics in Medical Image Segmentationcited by 58
  19. 2021Probabilistic Margins for Instance Reweighting in Adversarial Trainingcited by 57
  20. 2023Quantum machine learning for image classificationcited by 56
  21. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  22. 2022MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray imagescited by 53
  23. 2021Soft-Label Anonymous Gastric X-ray Image Distillationcited by 51
  24. 2022Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvementcited by 48
  25. 2022Boosting Out-of-distribution Detection with Typical Featurescited by 48
  26. 2021Balanced-MixUp for Highly Imbalanced Medical Image Classificationcited by 46
  27. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  28. 2021MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learningcited by 45
  29. 2021Source-Free Domain Adaptation for Image Segmentationcited by 45
  30. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  31. 2022PseudoClick: Interactive Image Segmentation with Click Imitationcited by 42
  32. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  33. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  34. 2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
  35. 2023Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Modelscited by 40
  36. 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
  37. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  38. 2021Polarized Self-Attention: Towards High-quality Pixel-wise Regressioncited by 39

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