Data Augmentation for Skin Lesion AnalysisData Augmentation for Skin Lesion AnalysisFábio Perez and Cristina Vasconcelos and Sandra Avila and Eduardo Valle2018
Paper summaryfabioperez_Disclaimer: I'm the first author of this paper._
The code for this paper can be found at https://github.com/fabioperez/skin-data-augmentation.
In this work, we wanted to compare different data augmentation scenarios for skin lesion analysis. We tried 13 scenarios, including commonly used augmentation techniques (color and geometry transformations), unusual ones (random erasing, elastic transformation, and a novel lesion mix to simulate collision lesions), and a combination of those.
Examples of the augmentation scenarios:
a) no augmentation
b) color (saturation, contrast, and brightness)
c) color (saturation, contrast, brightness, and hue)
d) affine (rotation, shear, scaling)
e) random flips
f) random crops
g) random erasing
i) lesion mix
j) basic set (f, d, e, c)
k) basic set + erasing (f, g, d, e, c)
l) basic set + elastic (f, d, h, e, c)
m) basic set + mix (i, f, d, e, c)
We used the ISIC 2017 Challenge dataset (2000 training images, 150 validation images, and 600 test images).
We tried three network architectures: Inception-v4, ResNet-152, and DenseNet-161.
We also compared different test-time data augmentation methods: a) no augmentation; b) 144-crops; c) same data augmentation as training (64 augmented copies of the original image). Final prediction was the average of all augmented predictions.
* Basic set (combination of commonly used augmentations) is the best scenario.
* Data augmentation at test-time is very beneficial.
* Elastic is better than no augmentation, but when compared incorporated to the basic set, decreases the performance.
* The best result was better than the winner of the challenge in 2017, without using ensembling.
* Test data augmentation is very similar with 144-crop, but takes less images during prediction (64 vs 144), so it's faster.
# Impact of data augmentation on dataset sizes
We also used the basic set scenarios on different dataset sizes by sampling random subsets of the original dataset, with sizes 1500, 1000, 500, 250 and 125.
* Using data augmentation can be better than using more data (but you should always use more data since the model can benefit from both). For instance, using 500 images with data augmentation on training and test for Inception is better than training with no data augmentation with 2000 images.
* ResNet and DenseNet works better than Inception for less data.
* Test-time data augmentation is always better than not augmenting on test-time.
* Using data augmentation on train only was worse than not augmenting at all in some cases.
Data Augmentation for Skin Lesion Analysis
arXiv e-Print archive - 2018 via Local arXiv
First published: 2018/09/05 (9 months ago) 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