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Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey

arXiv 2504.11588 ↗First posted 15 Apr 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research challenges.

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References · 93

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  5. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  9. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  10. 2016Improved Techniques for Training GANscited by 2,970
  11. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  12. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
38 more
  1. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  2. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  3. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  4. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  5. 2017A Closer Look at Memorization in Deep Networkscited by 942
  6. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 669
  7. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  8. 2020A Survey of Deep Active Learningcited by 554
  9. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  10. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 379
  11. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  12. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  13. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  14. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 216
  15. 2016Gland Segmentation in Colon Histology Images: The GlaS Challenge Contestcited by 203
  16. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  17. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 179
  18. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
  19. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  20. 2020Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularizationcited by 123
  21. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  22. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  23. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
  24. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 77
  25. 2019Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cubecited by 76
  26. 2019Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformationscited by 71
  27. 2019Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Predictioncited by 70
  28. 2021Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learningcited by 68
  29. 2020Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learningcited by 67
  30. 2019DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentationcited by 66
  31. 2020Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Modelcited by 59
  32. 20183D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Trainingcited by 58
  33. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
  34. 2021Positional Contrastive Learning for Volumetric Medical Image Segmentationcited by 56
  35. 2019Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentationcited by 55
  36. 2022BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotationcited by 54
  37. 2020FocalMix: Semi-Supervised Learning for 3D Medical Image Detectioncited by 52
  38. 2021Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentationcited by 52

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