Data Augmentation for Skin Lesion Analysis

TL;DR

This paper evaluates 13 data augmentation techniques for skin lesion classification, demonstrating that augmentation significantly improves model performance and can outperform models trained with additional data.

Contribution

It systematically compares various augmentation strategies, including novel methods, and highlights their impact on melanoma classification accuracy.

Findings

Data augmentation improves melanoma classification performance.

Test-time augmentation enhances model accuracy.

Augmentation can outperform additional data collection.

Abstract

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.