  Melanoma is a curable aggressive skin cancer if detected early. Typically,
the diagnosis involves initial screening with subsequent biopsy and
histopathological examination if necessary. Computer aided diagnosis offers an
objective score that is independent of clinical experience and the potential to
lower the workload of a dermatologist. In the recent past, success of deep
learning algorithms in the field of general computer vision has motivated
successful application of supervised deep learning methods in computer aided
melanoma recognition. However, large quantities of labeled images are required
to make further improvements on the supervised method. A good annotation
generally requires clinical and histological confirmation, which requires
significant effort. In an attempt to alleviate this constraint, we propose to
use categorical generative adversarial network to automatically learn the
feature representation of dermoscopy images in an unsupervised and
semi-supervised manner. Thorough experiments on ISIC 2016 skin lesion chal-
lenge demonstrate that the proposed feature learning method has achieved an
average precision score of 0.424 with only 140 labeled images. Moreover, the
proposed method is also capable of generating real-world like dermoscopy
images.
