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A comprehensive survey on deep active learning in medical image analysis

arXiv 2310.14230 ↗First posted 22 Oct 2023cs.CVcited by 32

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Abstract (arXiv)

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 168 of its 286 references that are in the map. First posted on arXiv 22 Oct 2023.

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168 of 286 references.

References · 168

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  7. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  8. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  9. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  10. 2017On Calibration of Modern Neural Networkscited by 4,860
  11. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  12. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
38 more
  1. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  4. 2022Visual Prompt Tuningcited by 1,674
  5. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  7. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  8. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  9. 2017Deep Bayesian Active Learning with Image Datacited by 812
  10. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  11. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  12. 2021The Medical Segmentation Decathloncited by 620
  13. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  14. 2020A Survey of Deep Active Learningcited by 547
  15. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  16. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  17. 2021Deep Long-Tailed Learning: A Surveycited by 416
  18. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  19. 2017Snapshot Ensembles: Train 1, get M for freecited by 349
  20. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  21. 2019Learning Loss for Active Learningcited by 335
  22. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  23. 2019Variational Adversarial Active Learningcited by 284
  24. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  25. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  26. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
  27. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  28. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  29. 2017Cost-Effective Active Learning for Deep Image Classificationcited by 165
  30. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 162
  31. 2018Adversarial Active Learning for Deep Networks: a Margin Based Approachcited by 155
  32. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 150
  33. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  34. 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 145
  35. 2020Contextual Diversity for Active Learningcited by 139
  36. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  37. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  38. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 13; 2026: 15.

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  1. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  2. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  3. 2025Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHFcited by 5
  4. 2025RAU: Reference-based Anatomical Understanding with Vision Language Modelscited by 3
  5. 2025Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capablescited by 2
  6. 2025To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Modelscited by 2
  7. 2025Model-agnostic Selective Labeling with Provable Statistical Guaranteescited by 2
  8. 2025ORCHID: Orchestrated Retrieval-Augmented Classification with Human-in-the-Loop Intelligent Decision-Making for High-Risk Propertycited by 1
  9. 2026From Cold Start to Active Learning: Embedding-Based Scan Selection for Medical Image Segmentationcited by 1
  10. 2026Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learningcited by 1
  11. 2026Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosiscited by 1
  12. 2026Open-Ended CT Volume Segmentation with Weak Supervision from Languagecited by 1
20 more
  1. 2023Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slidescited by 0
  2. 2024Salutary Labeling with Zero Human Annotationcited by 0
  3. 2025Weakly Supervised Pixel-Level Annotation with Visual Interpretabilitycited by 0
  4. 2025Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi-modal Gross Tumor Volume Segmentationcited by 0
  5. 2025nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentationcited by 0
  6. 2025Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentationcited by 0
  7. 2025Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentationcited by 0
  8. 2025Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedbackcited by 0
  9. 2025Data Efficiency and Transfer Robustness in Biomedical Image Segmentation: A Study of Redundancy and Forgetting with Cellposecited by 0
  10. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0
  11. 2026Active Label Cleaning for Reliable Detection of Electron Dense Deposits in Transmission Electron Microscopy Imagescited by 0
  12. 2026EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Modelscited by 0
  13. 2026Exploring Entropy-based Active Learning for Fair Brain Segmentationcited by 0
  14. 2026Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentationcited by 0
  15. 2026One-Shot Data Selection for Medical Image Classification via Graph Coveragecited by 0
  16. 2026Slide-Level Active Learning Reduces Annotation Burden in H&E imagescited by 0
  17. 2026In-Context Learning for Wound Classification with Small Multimodal Language Modelscited by 0
  18. 2026Active few-shot segmentation by reinforcing data selectioncited by 0
  19. 2026Diffusion-Based Tumor Inpainting for Renal Segmentation under Clinical Data Scarcitycited by 0
  20. 2026Low-Budget Active Learning through Entropic Optimal Transportcited by 0