iToBoS project held its 4th Project Management Board meeting


The iToBoS 4th Project Management Board (PMB) meeting took place on 18/04/2023 throughout a video conference system, with the participation of 19 attendees, including the Project Coordinator (PC), Project Manager (PM), Innovation and Exploitation Manager (IEM), Dissemination and Communication Manager (DCM), Data Manager (DM) and WP leaders.

XAI Hyperparameter Optimization


Rule-based eXplainable AI (XAI) methods, such as layer-wise relevance propagation (LRP) and DeepLift, provide large flexibility thanks to configurable rules, allowing AI practitioners to tailor the XAI method to the problem at hand.

DenseNet Canonization


As we saw in a previous post, some challenges caused in explaining neural network decisions can be overcome via canonization.

What happens when you’re diagnosed with melanoma?


iToBoS is aiming to streamline melanoma diagnosis, but what happens once you are diagnosed with melanoma?

The fourth issue of the iToBoS newsletter has been launched


Offering a handful of assorted articles and updates, this release offers content about the project, technology and trends.

Optical Technologies for Skin Cancer Detection


Optical technologies are a promising tool for the early detection of melanoma, which is a type of skin cancer that can be aggressive and deadly if not detected and treated in its early stages.

Skin cancer early detection with assistance of artificial intelligence


Skin cancer is the most common cancer in the world.

Risk scores in melanoma detection


Melanoma is the third most common cancer in Australia, but Australians have widely variable risk of developing melanoma. This makes it hard to recommend a one-size-fits-all approach to early detection.

iToBoS project presented in the Spanish Academic group for imaging in dermatology and venereology


iToBoS was presented at the “XI Reunion del grupo de trabajo de e-dermatologia e imagen (GEDEI) de la Academia Española de Dermatologia y Venereologia”. 

Digital Hair Removal


Digital hair removal is a technique that is used to improve the accuracy and reliability of dermoscopic images of melanoma, which is a type of skin cancer.