mlsanityData to 7 Oct 2026

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

arXiv 2107.09543 ↗First posted 20 Jul 2021eess.IV cs.AI cs.CVcited by 15

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Abstract (arXiv)

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in Generative Adversarial Networks (GANs), data synthesis, and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 198 of its 513 references that are in the map. First posted on arXiv 20 Jul 2021.

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198 of 513 references.

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Citing papers per year: 2022: 2; 2023: 3; 2024: 8; 2025: 2.

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  1. 2024A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentationscited by 40
  2. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  3. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  4. 2025MRI-CORE: A Foundation Model for Magnetic Resonance Imagingcited by 12
  5. 2022High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detectioncited by 11
  6. 2023Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentationcited by 9
  7. 2023Cross-modal tumor segmentation using generative blending augmentation and self trainingcited by 6
  8. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  9. 2024A Catalog of Fairness-Aware Practices in Machine Learning Engineeringcited by 4
  10. 2024Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Datacited by 3
  11. 2025Federated nnU-Net for Privacy-Preserving Medical Image Segmentationcited by 3
  12. 2023Method for Generating Synthetic Data Combining Chest Radiography Images with Tabular Clinical Information Using Dual Generative Modelscited by 1
3 more
  1. 2024Mitigating annotation shift in cancer classification using single image generative modelscited by 0
  2. 2024Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakescited by 0
  3. 2024Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Imagecited by 0