Dermoscopic skin images are often obtained with different imaging devices,
under varying acquisition conditions. In this work, instead of attempting to
perform intensity and color normalization, we propose to leverage computational
color constancy techniques to build an artificial data augmentation technique
suitable for this kind of images. Specifically, we apply the \emph{shades of
gray} color constancy technique to color-normalize the entire training set of
images, while retaining the estimated illuminants. We then draw one sample from
the distribution of training set illuminants and apply it on the normalized
image. We employ this technique for training two deep convolutional neural
networks for the tasks of skin lesion segmentation and skin lesion
classification, in the context of the ISIC 2017 challenge and without using any
external dermatologic image set. Our results on the validation set are
promising, and will be supplemented with extended results on the hidden test
set when available.

提出了一种利用计算机色彩恒常性技术构建人工数据增强技术的方法，用于皮肤皮损分割和皮肤皮损分类的深度卷积神经网络训练中，以及在不使用任何外部皮肤图像集的情况下，在 ISIC 2017 挑战赛的背景下进行颜色归一化的方法。