Quality check in Skin Cancer AI


Skin is the largest organ of human body and skin cancer is the most common cancer in the world. Skin cancer is deadly at late stage when cancer spreads to internal organs. However, if being detected early, skin cancer can be cured with high survival rate.

Round table on Ethical AI


iToBoS project recently took part in a round table discussion on the ethical use of artificial intelligence (AI) in city infrastructure and planning, led by the Economics Program at the Center for Strategic and International Studies (CSIS).

Image Colour Correction


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.

Digital Communication Workshop


iToBoS organized an internal workshop on digital communication on May 31, 2022, via video conferencing system.

Applying Artificial Intelligence Privacy Technology in the Healthcare Domain


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

iToBoS project presented in the Medical Informatics Europe Congress 2022


iToBoS project was presented at the conference "Applying AI Privacy Technology in the Healthcare Domain", in the Medical Informatics Europe Congress (MIE 2022).

Extraordinary General Assembly (GA) meeting


An extraordinary General Assembly (GA) meeting took place on 30th May 2022 throughout video conference system with the attendance of 28 participants belonging to the 19 project partner organizations.

Convolutional Neural Networks (CNN) for image processing


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

[FR] iToBoS presented in Actu Toulouse


iToBoS project was presented on May 25, 2022, in Actu Toulouse. The article is written in French.

An Introduction to Generative Adversarial Networks (GAN)


Generative Adversarial Networks are unsupervised neural networks that are able to analyse information from a dataset and produce similar new samples.