Unsupervised Feature Elimination via Generative Adversarial Networks: Application to Hair Removal in Melanoma Classification

Abstract

Eliminating the undesirable features is crucial to computer vision applications since undesirable features degrade the visibility of images. For that purpose, denoising, dehazing and deraining have been actively studied in both traditional model-based approaches and modern deep learning methods. However, elimination of hair in dermoscopic images has not received sufficient attention despite its significance and potential. Meanwhile, hair removal algorithms remain within the classical morphological methodologies, while only a few attempts apply the latest data-driven techniques. Hair is desired to be removed in dermoscopy applications because it causes undesired effects such as occlusions in lesion areas. However, removing hair is challenging because of its inherent complex structure and variations. In this paper, we propose a new unsupervised algorithm for hair removal and evaluate it on a real-world melanoma dataset. The proposed method eliminates hair from dermoscopic images by inducing a reconstructed distribution of images with hair to resemble a hairless distribution using generative adversarial learning. In the generative adversarial learning framework, hair features are characterized with a coarse-grained label simply via a binary classifier. At the same time, the important features of the lesions are preserved by minimizing L-1-norm reconstruction loss based on Laplace noise assumption. The qualitative evaluation of the hair-removed results show that the proposed algorithm is robust to hair variations, and the quantitative results demonstrate that applying our hair removal algorithm considerably improves the performance of melanoma classification, outperforming the benchmarks.