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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'>Github</a>.</br></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.</br></br>
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.
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