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| This is a demo of <a href="https://arxiv.org/abs/2004.06824">Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> (Physics in Medicine and Biology, 2020).</br> | |
| 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 <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a>.<br> | |
| This method was also effective for COVID-19 detection from chest radiography images which led to | |
| <a href="https://github.com/hasibzunair/synthetic-covid-cxr-dataset">Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis</a>. | |
| 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. | |
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