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.

本研究以 ISBI 2017 黑色素瘤分类挑战为背景，通过人工增强图像数据和 CNN 模型委员会的方法来提高皮肤癌早期检测的准确率和分类器的稳定性。