A Deep Embedded Framework for Spitzoid Neoplasm Classification Using DNA Methylation Data

Abstract

[EN] Spitzoid melanocytic tumors (SMT) are a group of neoplasms that represent a formidable diagnostic challenge for dermatopathologists. DNA methylation (DNAm) is a welldefined epigenetic factor that has an important role in the development of these lesions. In this work, we propose different deep-learning-based approaches to address the Spitzoid neoplasms detection from DNAm. We use an autoencoder and a variational autoencoder for dimensionality reduction with a subsequently supervised classification. Additionally, we present a deep embedded refined clustering algorithm able to optimize the latent space at the same time that the non-supervised classification task is performed. This novel approach in DNAm supposes a step forward in the SMT detection as suggest the obtained results (acc = 0.9). Additionally, making use of the resulting model, we present a subspace-prototypical-based approach for the prognostic prediction of uncertain malignant potential samples, which is nowadays the hottest open area in SMT detection.