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

TL;DR

This paper introduces a novel categorical GAN approach assisted by Wasserstein distance for dermoscopy image classification, enabling effective unsupervised and semi-supervised learning with limited labeled data, and generating realistic dermoscopy images.

Contribution

It presents a new GAN-based method that reduces the need for extensive labeled data in melanoma detection and can generate realistic dermoscopy images.

Findings

Achieved an average precision of 0.424 with only 140 labeled images.

Successfully generated realistic dermoscopy images.

Demonstrated effectiveness on ISIC 2016 skin lesion challenge.

Abstract

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.