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

Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge