Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
This work addresses color variability issues in dermatologic image analysis, offering a domain-specific solution that is incremental in nature.
The paper tackled the problem of varying color and intensity in dermoscopic skin images by proposing a data-driven color augmentation technique based on computational color constancy, which improved performance in skin lesion segmentation and classification tasks on the ISIC 2017 challenge.
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.