# Melanoma Detection using Adversarial Training and Deep Transfer Learning

arXiv: https://arxiv.org/abs/2004.06824 · HTML: https://mlsanity.com/paper/2004.06824/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)
- [Knowledge Distillation approach towards Melanoma Detection](https://mlsanity.com/paper/2210.08086/) (2022)
- [ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans](https://mlsanity.com/paper/2105.12810/) (2021)
- [Generation of microbial colonies dataset with deep learning style transfer](https://mlsanity.com/paper/2111.03789/) (2021)
- [BioGAN: An unpaired GAN-based image to image translation model for microbiological images](https://mlsanity.com/paper/2306.06217/) (2023)
- [Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis](https://mlsanity.com/paper/2106.09759/) (2021)
- [STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification](https://mlsanity.com/paper/2108.08362/) (2021)
- [CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI](https://mlsanity.com/paper/2503.02345/) (2025)
- [Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets](https://mlsanity.com/paper/2103.12068/) (2021)
- [GAN-based generative modelling for dermatological applications -- comparative study](https://mlsanity.com/paper/2208.11702/) (2022)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)
- [A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets](https://mlsanity.com/paper/2302.09528/) (2023)
- [Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification](https://mlsanity.com/paper/2403.06798/) (2024)
- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)

## Abstract (arXiv)

Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning toward melanoma detection. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation. In the second stage, we train a deep convolutional neural network for skin lesion classification using the original training set combined with the newly synthesized under-represented class samples. The training of this classifier is carried out by minimizing the focal loss function, which assists the model in learning from hard examples, while down-weighting the easy ones. Experiments conducted on a dermatology image benchmark demonstrate the superiority of our proposed approach over several standard baseline methods, achieving significant performance improvements. Interestingly, we show through feature visualization and analysis that our method leads to context based lesion assessment that can reach an expert dermatologist level.