  Skin cancer is a serious condition that requires accurate diagnosis and
treatment. One way to assist clinicians in this task is using computer-aided
diagnosis (CAD) tools that automatically segment skin lesions from dermoscopic
images. We propose a novel adversarial learning-based framework called
Efficient-GAN (EGAN) that uses an unsupervised generative network to generate
accurate lesion masks. It consists of a generator module with a top-down
squeeze excitation-based compound scaled path, an asymmetric lateral
connection-based bottom-up path, and a discriminator module that distinguishes
between original and synthetic masks. A morphology-based smoothing loss is also
implemented to encourage the network to create smooth semantic boundaries of
lesions. The framework is evaluated on the International Skin Imaging
Collaboration (ISIC) Lesion Dataset 2018. It outperforms the current
state-of-the-art skin lesion segmentation approaches with a Dice coefficient,
Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. We
also design a lightweight segmentation framework (MGAN) that achieves
comparable performance as EGAN but with an order of magnitude lower number of
training parameters, thus resulting in faster inference times for low compute
resource settings.
