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

arXiv 2010.05351 ↗First posted 11 Oct 2020cs.CVcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Oct 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 9; 2024: 2; 2025: 4; 2026: 3.

24 papers.

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  1. 2023Does progress on ImageNet transfer to real-world datasets?cited by 35
  2. 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspectivecited by 20
  3. 2021Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classificationcited by 18
  4. 2023Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanomacited by 18
  5. 2023Simplifying Neural Network Training Under Class Imbalancecited by 18
  6. 2021Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoderscited by 4
  7. 2023Inventing a coin-flip classifier: Accounting for multiplicity in machine learning benchmark performancecited by 2
  8. 2023Data augmentation and explainability for bias discovery and mitigation in deep learningcited by 2
  9. 2024Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Studycited by 1
  10. 2025An analysis of data variation and bias in image-based dermatological datasets for machine learning classificationcited by 1
  11. 2025On the Role of Calibration in Benchmarking Algorithmic Fairness for Skin Cancer Detectioncited by 1
  12. 2021Morphological Classification of Galaxies in S-PLUS using an Ensemble of Convolutional Networkscited by 0
12 more
  1. 2022Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classificationcited by 0
  2. 2022An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseasescited by 0
  3. 2022Leveraging Contextual Data Augmentation for Generalizable Melanoma Detectioncited by 0
  4. 2023Transformer-based interpretable multi-modal data fusion for skin lesion classificationcited by 0
  5. 2023Revising deep learning methods in parking lot occupancy detectioncited by 0
  6. 2023Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Imagescited by 0
  7. 2024Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliencycited by 0
  8. 2025Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentationcited by 0
  9. 2025The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methodscited by 0
  10. 2026Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networkscited by 0
  11. 2026Style-Based Neural Architectures for Real-Time Weather Classificationcited by 0
  12. 2026Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Userscited by 0