# Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images

arXiv: https://arxiv.org/abs/1610.04662 · HTML: https://mlsanity.com/paper/1610.04662/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2016.

## Built on by (most cited first, 26 of 26)

- [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning](https://mlsanity.com/paper/2204.11433/) (2022)
- [RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection](https://mlsanity.com/paper/2211.04793/) (2022)
- [Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models](https://mlsanity.com/paper/2311.14339/) (2023)
- [A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information](https://mlsanity.com/paper/2104.14353/) (2021)
- [Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots](https://mlsanity.com/paper/2306.12898/) (2023)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [An Attention-based Weakly Supervised framework for Spitzoid Melanocytic Lesion Diagnosis in WSI](https://mlsanity.com/paper/2104.09878/) (2021)
- [Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning](https://mlsanity.com/paper/2303.14615/) (2023)
- [Early Melanoma Diagnosis with Sequential Dermoscopic Images](https://mlsanity.com/paper/2110.05976/) (2021)
- [Testing the Consistency of Performance Scores Reported for Binary Classification Problems](https://mlsanity.com/paper/2310.12527/) (2023)
- [Dermoscopic Image Classification with Neural Style Transfer](https://mlsanity.com/paper/2105.07592/) (2021)
- [Out of distribution detection for skin and malaria images](https://mlsanity.com/paper/2111.01505/) (2021)
- [Classification of Melanocytic Nevus Images using BigTransfer (BiT)](https://mlsanity.com/paper/2211.11872/) (2022)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)
- [Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion](https://mlsanity.com/paper/2305.09542/) (2023)
- [On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning](https://mlsanity.com/paper/2408.03508/) (2024)
- [A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation](https://mlsanity.com/paper/2501.01991/) (2024)
- [Model Checking in Medical Imaging for Tumor Detection and Segmentation](https://mlsanity.com/paper/2501.02024/) (2025)
- [MLMC: Interactive multi-label multi-classifier evaluation without confusion matrices](https://mlsanity.com/paper/2501.14460/) (2025)
- [Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification](https://mlsanity.com/paper/2508.11511/) (2025)
- [ViConEx-Med: Visual Concept Explainability via Multi-Concept Token Transformer for Medical Image Analysis](https://mlsanity.com/paper/2510.10174/) (2025)
- [LesionTABE: Equitable AI for Skin Lesion Detection](https://mlsanity.com/paper/2601.03090/) (2026)
- [OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis](https://mlsanity.com/paper/2607.25108/) (2026)

## Abstract (arXiv)

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).