Controlled Synthesis of Dermoscopic Images via a New Color Labeled Generative Style Transfer Network to Enhance Melanoma Segmentation.

Dermoscopic imaging is an established technique to detect, track, and diagnose malignant melanoma, and one of the ways to improve this technique is via computer-aided image segmentation. Image segmentation is an important step towards building computerized detection and classification systems by delineating the area of interest, in our case, the skin lesion, from the background. However, current segmentation techniques are hard pressed to account for color artifacts within dermoscopic images that are often incorrectly detected as part of the lesion. Often there are few annotated examples of these artifacts, which limits training segmentation methods like the fully convolutional network (FCN) due to the skewed dataset. We propose to improve FCN training by augmenting the dataset with synthetic images created in a controlled manner using a generative adversarial network (GAN). Our novelty lies in the use of a color label (CL) to specify the different characteristics (approximate size, location, and shape) of the different regions (skin, lesion, artifacts) in the synthetic images. Our GAN is trained to perform style transfer of real melanoma image characteristics (e.g. texture) onto these color labels, allowing us to generate specific types of images containing artifacts. Our experimental results demonstrate that the synthetic images generated by our technique have a lower mean average error when compared to synthetic images generated using traditional binary labels. As a consequence, we demonstrated improvements in melanoma image segmentation when using synthetic images generated by our technique.