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”.

iToBoS presented through the poster "Exosome micro RNAs as liquid biopsy biomarkers to follow-up skin melanomas patients".

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.

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.

Image colour transfer aims to alter the colours of one image (source) to mimic the appearance and colour palette of another one (target).

In this blogpost we will talk about the importance of image segmentation for dermoscopy as well as other medical imaging techniques. For skin cancer screening it is of importance to segment the nevus from the skin.

Image Super-Resolution is the task of restoring a high-resolution image out of one or several lower resolution images. A simple enlargement of the image would produce a blurred result, so a special approach is needed to boost the apparent resolution and enhance image sharpness.