This is a demo of Melanoma Detection using Adversarial Training and Deep Transfer Learning (Physics in Medicine and Biology, 2020).
We introduce an over-sampling method for learning the inter-class mapping between under-represented class samples and over-represented samples in a bid to generate under-represented class samples using unpaired image-to-image translation. These synthetic images are then used as additional training data in the task of detecting abnormalities in binary classification use-cases. Code is publicly available in Github.

This method was also effective for COVID-19 detection from chest radiography images which led to Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis. The synthetic images not only improved performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence.

This demo model predicts if the given image has benign or malignant symptoms. To use it, simply upload a skin lesion image, or click one of the examples to load them. Read more at the links below.