  We present our winning solution to the SIIM-ISIC Melanoma Classification
Challenge. It is an ensemble of convolutions neural network (CNN) models with
different backbones and input sizes, most of which are image-only models while
a few of them used image-level and patient-level metadata. The keys to our
winning are: (1) stable validation scheme (2) good choice of model target (3)
carefully tuned pipeline and (4) ensembling with very diverse models. The
winning submission scored 0.9600 AUC on cross validation and 0.9490 AUC on
private leaderboard.
