  Skin cancer, the most commonly found human malignancy, is primarily diagnosed
visually via dermoscopic analysis, biopsy, and histopathological examination.
However, unlike other types of cancer, automated image classification of skin
lesions is deemed more challenging due to the irregularity and variability in
the lesions' appearances. In this work, we propose an adaptation of the Neural
Style Transfer (NST) as a novel image pre-processing step for skin lesion
classification problems. We represent each dermoscopic image as the style image
and transfer the style of the lesion onto a homogeneous content image. This
transfers the main variability of each lesion onto the same localized region,
which allows us to integrate the generated images together and extract latent,
low-rank style features via tensor decomposition. We train and cross-validate
our model on a dermoscopic data set collected and preprocessed from the
International Skin Imaging Collaboration (ISIC) database. We show that the
classification performance based on the extracted tensor features using the
style-transferred images significantly outperforms that of the raw images by
more than 10%, and is also competitive with well-studied, pre-trained CNN
models through transfer learning. Additionally, the tensor decomposition
further identifies latent style clusters, which may provide clinical
interpretation and insights.
