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Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

arXiv 1806.05473 ↗First posted 14 Jun 2018cs.CVcited by 74

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Abstract (arXiv)

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2018.

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Citing papers per year: 2021: 21; 2022: 21; 2023: 15; 2024: 8; 2025: 9.

74 papers.

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  1. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  2. 2021Semi-Supervised Active Learning with Temporal Output Discrepancycited by 50
  3. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
  4. 2021Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labelscited by 43
  5. 2023Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigmcited by 36
  6. 2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
  7. 2021Consistency-based Active Learning for Object Detectioncited by 32
  8. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  9. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  10. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  11. 2023Active learning for medical image segmentation with stochastic batchescited by 20
  12. 2021Boosting Active Learning via Improving Test Performancecited by 19
38 more
  1. 2021MCDAL: Maximum Classifier Discrepancy for Active Learningcited by 18
  2. 2021Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentationcited by 17
  3. 2022Semantic Segmentation with Active Semi-Supervised Learningcited by 16
  4. 2022Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Frameworkcited by 14
  5. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  6. 2022Deep Active Learning with Noise Stabilitycited by 13
  7. 2022Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applicationscited by 11
  8. 2023Semantic Image Synthesis for Abdominal CTcited by 10
  9. 2024Generative Active Learning for Long-tailed Instance Segmentationcited by 10
  10. 2021Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Imagescited by 9
  11. 2023This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generationcited by 9
  12. 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
  13. 2021Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectivescited by 7
  14. 2021When Deep Learners Change Their Mind: Learning Dynamics for Active Learningcited by 6
  15. 2021CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentationcited by 6
  16. 2021Towards General and Efficient Active Learningcited by 6
  17. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  18. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  19. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  20. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
  21. 2023OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classificationcited by 5
  22. 2025Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approachcited by 5
  23. 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
  24. 2022Task-Aware Active Learning for Endoscopic Image Analysiscited by 4
  25. 2022Factors that influence the adoption of human-AI collaboration in clinical decision-makingcited by 4
  26. 2023Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initializationcited by 4
  27. 2024Label Critic: Design Data Before Modelscited by 4
  28. 2022Deep reinforced active learning for multi-class image classificationcited by 3
  29. 2022MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detectioncited by 3
  30. 2023A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labelscited by 3
  31. 20213-D PET Image Generation with tumour masks using TGANcited by 2
  32. 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
  33. 2022Temporal Output Discrepancy for Loss Estimation-based Active Learningcited by 2
  34. 2024Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasetscited by 2
  35. 2024Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Samplingcited by 2
  36. 2025Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasetscited by 2
  37. 2025Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesiscited by 2
  38. 2022GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Frameworkcited by 1

50 most cited of 74 All 74 (JSON)