# Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network

arXiv: https://arxiv.org/abs/1910.11960 · HTML: https://mlsanity.com/paper/1910.11960/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Generative Adversarial Networks for Data Augmentation](https://mlsanity.com/paper/2306.02019/) (2023)
- [LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation](https://mlsanity.com/paper/2308.09026/) (2023)
- [Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease](https://mlsanity.com/paper/2107.12775/) (2021)
- [Multimodal Fusion Learning with Dual Attention for Medical Imaging](https://mlsanity.com/paper/2412.01248/) (2024)
- [A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis](https://mlsanity.com/paper/2210.06334/) (2022)
- [Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images](https://mlsanity.com/paper/2309.12245/) (2023)
- [Explainable AI the Latest Advancements and New Trends](https://mlsanity.com/paper/2505.07005/) (2025)
- [Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects](https://mlsanity.com/paper/2109.03391/) (2021)
- [SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions](https://mlsanity.com/paper/2507.19970/) (2025)
- [HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2511.17614/) (2025)
- [Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention](https://mlsanity.com/paper/2607.19086/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) show a significant impact on medical imaging. One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. This problem hinders the training of robust and well-generalizing models. Data Augmentation addresses this by using existing data more effectively. However, standard data augmentation implementations are manually designed and produce only limited reasonably alternative data. Instead, Generative Adversarial Networks (GANs) is utilized to generate a much broader set of augmentations. This paper proposes a novel enhancement for the progressive generative adversarial networks (PGAN) using self-attention mechanism. Self-attention mechanism is used to directly model the long-range dependencies in the feature maps. Accordingly, self-attention complements PGAN to generate fine-grained samples that comprise clinically-meaningful information. Moreover, the stabilization technique was applied to the enhanced generative model. To train the generative models, ISIC 2018 skin lesion challenge dataset was used to synthesize highly realistic skin lesion samples for boosting further the classification result. We achieve an accuracy of 70.1% which is 2.8% better than the non-augmented one of 67.3%.