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Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

arXiv 1801.02385 ↗First posted 8 Jan 2018cs.CVcited by 136

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Abstract (arXiv)

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.

Key facts. Cited by 136 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jan 2018.

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Citing papers per year: 2021: 24; 2022: 31; 2023: 33; 2024: 23; 2025: 19; 2026: 6.

136 papers.

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  1. 2022Kubric: A scalable dataset generatorcited by 373
  2. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 235
  3. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  4. 2022Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detectioncited by 172
  5. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  6. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  7. 2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
  8. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  9. 2024Best Practices and Lessons Learned on Synthetic Datacited by 104
  10. 2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
  11. 2021Context Decoupling Augmentation for Weakly Supervised Semantic Segmentationcited by 69
  12. 2021Ensembling with Deep Generative Viewscited by 53
38 more
  1. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  2. 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
  3. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  4. 2022A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledgecited by 31
  5. 2022FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classificationcited by 30
  6. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  7. 2023Green Edge AI: A Contemporary Surveycited by 24
  8. 2024Understanding Test-Time Augmentationcited by 23
  9. 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Modelscited by 22
  10. 2021A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learningcited by 19
  11. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  12. 2021Free-form tumor synthesis in computed tomography images via richer generative adversarial networkcited by 16
  13. 2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
  14. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  15. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  16. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  17. 2021Jekyll: Attacking Medical Image Diagnostics using Deep Generative Modelscited by 10
  18. 2021Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regressioncited by 10
  19. 2024Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencodercited by 10
  20. 2022Self-Supervised Text Erasing with Controllable Image Synthesiscited by 9
  21. 2021The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognitioncited by 8
  22. 2022Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Surveycited by 8
  23. 2022Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Imagescited by 8
  24. 2023Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentationscited by 8
  25. 2023Utility Theory of Synthetic Data Generationcited by 8
  26. 2021Data Augmentation for Graph Convolutional Network on Semi-Supervised Classificationcited by 7
  27. 2022Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applicationscited by 7
  28. 2022FedSyn: Synthetic Data Generation using Federated Learningcited by 7
  29. 2023Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxycited by 7
  30. 2023Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusioncited by 7
  31. 2022Arrhythmia Classification using CGAN-augmented ECG Signalscited by 6
  32. 2022Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classificationcited by 6
  33. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  34. 2024Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodologycited by 6
  35. 2024SynFER: Towards Boosting Facial Expression Recognition with Synthetic Datacited by 6
  36. 2021Vision Transformer for femur fracture classificationcited by 5
  37. 2021GANmapper: geographical data translationcited by 5
  38. 2023Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferabilitycited by 5

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