mlsanityData to 7 Oct 2026

GAN-based generative modelling for dermatological applications -- comparative study

arXiv 2208.11702 ↗First posted 24 Aug 2022eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

The lack of sufficiently large open medical databases is one of the biggest challenges in AI-powered healthcare. Synthetic data created using Generative Adversarial Networks (GANs) appears to be a good solution to mitigate the issues with privacy policies. The other type of cure is decentralized protocol across multiple medical institutions without exchanging local data samples. In this paper, we explored unconditional and conditional GANs in centralized and decentralized settings. The centralized setting imitates studies on large but highly unbalanced skin lesion dataset, while the decentralized one simulates a more realistic hospital scenario with three institutions. We evaluated models' performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition we provided explainability through exploration of latent space and embeddings projection focused both on global and local explanations. Calculated distance between real images and their projections in the latent space proved the authenticity and generalization of trained GANs, which is one of the main concerns in this type of applications. The open source code for conducted studies is publicly available at urlhttps://github.com/aidotse/stylegan2-ada-pytorch.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 24 Aug 2022.

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References · 23

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11 more
  1. 2019XRAI: Better Attributions Through Regionscited by 116
  2. 2020Detect and Correct Bias in Multi-Site Neuroimaging Datasetscited by 44
  3. 2021GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Reviewcited by 25
  4. 2019Skin Lesion Synthesis with Generative Adversarial Networkscited by 22
  5. 2020Melanoma Detection using Adversarial Training and Deep Transfer Learningcited by 17
  6. 2020Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANscited by 17
  7. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
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  10. 2020MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Educationcited by 3
  11. 2020Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in datacited by 1

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