  This work is about the semantic segmentation of skin lesion boundary and
their attributes using Image-to-Image Translation with Conditional Adversarial
Nets. Melanoma is a type of skin cancer that can be cured if detected in time.
Segmentation into dermoscopic images is an essential procedure for
computer-assisted diagnosis due to its existing artifacts typical of skin
images. To alleviate the image annotation process, we propose to use a modified
Pix2Pix network. The discriminator network learns the mapping from a dermal
image as an input and a mask image of six channels as an output. Likewise, the
discriminative network output called PatchGAN is varied for one channel and six
output channels. The photos used come from the 2018 ISIC Challenge, where 500
photographs are used with their respective semantic map, divided into 75% for
training and 35% for testing. Obtaining for 100 training epochs high Jaccard
indices for all attributes of the segmentation map.
