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 obtain the state of the art results for the ISBI 2016 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.
In the field of medicine, data augmentation has been very recently applied in REF in relation with the ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards Melanoma Detection), successfully overcoming the small dimension and biased nature of the biological database.
Increasing Deep Learning Melanoma Classification by Classical And Expert Knowledge Based Image Transforms
ArXiv
ArXiv
Computer Science