论文标题
使用高效网集识别黑色素瘤图像：SIIM-ISIC黑色素瘤分类挑战的胜利解决方案
Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge
论文作者
论文摘要
我们为SIIM-ISIC黑色素瘤分类挑战提供了成功解决方案。它是具有不同骨架和输入尺寸的卷积神经网络（CNN）模型的合奏，其中大多数是仅图像模型，而其中一些则使用了图像级和患者级元数据。我们获胜的关键是：（1）稳定验证方案（2）模型目标的良好选择（3）经过精心调整的管道和（4）与非常多样化的模型结合。获胜的提交在交叉验证时得分为0.9600 AUC，在私人排行榜上为0.9490 AUC得分。
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.
