Generative Adversarial Networks are unsupervised neural networks that are able to analyse information from a dataset and produce similar new samples.
As observed by Dr Eline Noels of the Erasmus University of Rotterdam, the knowledge of the economic burden of skin cancers is “essential to enable health policy decision-makers to make well-informed decisions on potential interventions and to be able to evaluate the future effect of these decisions”.
For image denoising, there is an important line of work that uses the self-similarity principle that natural images obey: an image contains many image patches similar between each other. To remove noise, one can look for similar patches in an image and average them.
Machine learning and specifically deep learning, have dramatically improved the state-of-the-art in many areas of research, including computer vision, speech recognition, and natural language processing.
In EU 27, melanoma is the 6th type of cancer in terms of incidence (new cases per year) after breast, colorectum, prostate, lung and bladder and the 16th in terms of mortality (yearly deaths).
Training data balance is crucial to the performance of machine learning (ML) models, especially deep learning models. There would be a high risk of overfitting when training on unbalanced datasets.
Image inpainting is the technique of reconstructing of missing portions in an image in an undetectable way, restoring both texture and structure.
In the last few years, there have been remarkable developments in computational methods for helping dermatologists to diagnose skin cancer in early stages.
Image colour transfer aims to alter the colours of one image (source) to mimic the appearance and colour palette of another one (target).