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.

翻译：我们为SIIM-ISIC的梅兰诺马分类挑战展示了我们的胜利解决方案,这是一系列具有不同骨干和投入大小的神经网络(CNN)演变模型,其中多数是仅图象模型,少数使用图像水平和病人水平元数据,我们获胜的关键是:(1) 稳定的验证计划(2) 模型目标的妥善选择(3) 仔细调整管道和(4) 与非常多样化模型结合。获胜的提交书获得0.9600 ACU的交叉验证和0.9490 ACU的私人首板。