Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge
2020-10-11Code Available1· sign in to hype
Qishen Ha, Bo Liu, Fuxu Liu
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ReproduceCode
- github.com/haqishen/SIIM-ISIC-Melanoma-Classification-1st-Place-SolutionOfficialIn paperpytorch★ 309
- github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learningtf★ 169
- github.com/stanleyjzheng/masseyhacksviipytorch★ 3
- github.com/Ramstein/MelanomaClassificationpytorch★ 0
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| ISIC 2020 Challenge Dataset | EfficientNet Ensemble | AUC | 0.95 | — | Unverified |