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

arXiv: https://arxiv.org/abs/2208.11702 · HTML: https://mlsanity.com/paper/2208.11702/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Skin Lesion Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1902.03253/) (2019)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs](https://mlsanity.com/paper/2010.03975/) (2020)
- [Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary](https://mlsanity.com/paper/2101.07235/) (2021)
- [On the Fairness of Swarm Learning in Skin Lesion Classification](https://mlsanity.com/paper/2109.12176/) (2021)
- [Semi-Supervised Federated Peer Learning for Skin Lesion Classification](https://mlsanity.com/paper/2103.03703/) (2021)
- [MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education](https://mlsanity.com/paper/2010.05177/) (2020)
- [Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data](https://mlsanity.com/paper/2005.02269/) (2020)

## 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.