Image colour correction is the task of altering the colours in an image to match the wanted colours, usually the colours humans perceived at the scene acquisition. One popular way of doing that is by using a Colour Checker.

The scientific work "Applying Artificial Intelligence Privacy Technology in the Healthcare Domain", supported by iToBoS project, has been published.

Convolutional neural networks are a type of neural networks often used to perform machine learning techniques on images.

iToBoS project was presented in Actu Toulouse, a digital newspaper that publishes articles on the daily life of the inhabitants of Toulouse and its region, portraits, ideas for outings, tips and practical information. The article is written in French.

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.