Synthetic data generation in healthcare

Abstract

According to the World Health Organization, skin cancer represents approximately one third of every diagnosed cancer, reaching over 3 million cases over the world, annually. Similar to other types of cancer, though, early diagnosis is key to a good outcome, and computer-aided diagnosis has shown great promise in being able to recognise melanomas. Neural networks can be applied to distinguish between melanoma and non-melanoma cases in a few seconds, which makes it a great help to the diagnosing physician. But deep learning (DL) based approaches need a large amount of annotated data to perform well. In addition, the use of healthcare data in the development of DL models is associated with challenges relating to personal data and regulatory issues. Patient data cannot be freely shared and is therefore limited in its usefulness for creating AI solutions. All of these issues can be addressed using different methods, showing the potential of artificial data for such use cases.