Title: Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation,
Abstract: Skin cancer is a major public health problem, as is the most common type of
cancer and represents more than half of cancer diagnoses worldwide. Early
detection influences the outcome of the disease and motivates our work. We
investigate the composition of CNN committees and data augmentation for the the
ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards
Melanoma Detection) facing the peculiarities of dealing with such a small,
unbalanced, biological database. For that, we explore committees of
Convolutional Neural Networks trained over the ISBI challenge training dataset
artificially augmented by both classical image processing transforms and image
warping guided by specialist knowledge about the lesion axis and improve the
final classifier invariance to common melanoma variations.