Controllable Skin Lesion Synthesis Using Texture Patches, Bézier Curves and Conditional GANs

Data synthesis is an important tool for improving data availability in cases where data is hard to capture or annotate. In the context of skin lesions data, data synthesis has been used for data augmentation in automated classification methods or for supporting training of dermoscopic images visual inspection. In this paper, we propose a simple yet effective approach for diverse skin lesion image synthesis using conditional generative adversarial networks. Our pipeline takes as input a random Bézier curve representing the lesion mask, and two texture patches: one for skin, and one for lesion; and synthesizes a new dermoscopic image. Our method generates images where lesions and skin reproduce the corresponding provided texture patches, and the lesion conforms to the provided Bézier mask. Our results report realistic controllable synthesis and improved performance for skin lesion segmentation task considering different semantic segmentation networks in a public challenge in comparison to classic data augmentation.