# Data Augmentation for Skin Lesion Analysis

arXiv: https://arxiv.org/abs/1809.01442 · HTML: https://mlsanity.com/paper/1809.01442/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 28 of 28)

- [Ensembling with Deep Generative Views](https://mlsanity.com/paper/2104.14551/) (2021)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation](https://mlsanity.com/paper/2206.02307/) (2022)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing](https://mlsanity.com/paper/2103.06179/) (2021)
- [A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information](https://mlsanity.com/paper/2104.14353/) (2021)
- [Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images](https://mlsanity.com/paper/2106.14465/) (2021)
- [Test-time Augmentation for Factual Probing](https://mlsanity.com/paper/2310.17121/) (2023)
- [S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2412.13156/) (2024)
- [Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2105.05516/) (2021)
- [U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation](https://mlsanity.com/paper/2210.16399/) (2022)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study](https://mlsanity.com/paper/2306.02800/) (2023)
- [Test-Time Selection for Robust Skin Lesion Analysis](https://mlsanity.com/paper/2308.05595/) (2023)
- [Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI](https://mlsanity.com/paper/2401.06893/) (2024)
- [When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out](https://mlsanity.com/paper/2404.12295/) (2024)
- [DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation](https://mlsanity.com/paper/2602.19848/) (2026)
- [Joint Dermatological Lesion Classification and Confidence Modeling with Uncertainty Estimation](https://mlsanity.com/paper/2107.08770/) (2021)
- [PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Prediction](https://mlsanity.com/paper/2310.18268/) (2023)
- [Collinear datasets augmentation using Procrustes validation sets](https://mlsanity.com/paper/2312.04911/) (2023)
- [DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile](https://mlsanity.com/paper/2511.10367/) (2025)
- [Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification](https://mlsanity.com/paper/2607.26765/) (2026)
- [ABO-Med: Accelerated Bilevel Optimization for Few-Shot Medical Image Classification](https://mlsanity.com/paper/2609.33176/) (2026)
- [Impact of Data Augmentation on Confidence Calibration in Melanoma Classification](https://mlsanity.com/paper/2610.06146/) (2026)

## Abstract (arXiv)

Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.