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

arXiv: https://arxiv.org/abs/2010.05351 · HTML: https://mlsanity.com/paper/2010.05351/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 25 of 25)

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- [Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification](https://mlsanity.com/paper/2207.07303/) (2022)
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- [The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods](https://mlsanity.com/paper/2511.21363/) (2025)
- [Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks](https://mlsanity.com/paper/2603.13497/) (2026)
- [Style-Based Neural Architectures for Real-Time Weather Classification](https://mlsanity.com/paper/2604.18251/) (2026)
- [Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users](https://mlsanity.com/paper/2605.18036/) (2026)
- [Impact of Data Augmentation on Confidence Calibration in Melanoma Classification](https://mlsanity.com/paper/2610.06146/) (2026)

## Abstract (arXiv)

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.