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

Unsupervised and semi-supervised learning with Categorical Generative  Adversarial Networks assisted by Wasserstein distance for dermoscopy image  Classification