A step into Machine Unlearning


What is Machine Unlearning? Basically, this concept represents the opposite of machine learning: it serves to make a model unlearn or forget.

iToBoS participated in ISE Open Innovation Challenge 2024


The iToBoS project participated in the ISE Open Innovation Challenge 2024, an international B2B meeting event that took place both in Barcelona, in the venue of the ISE Congress, and online, between January 30 and February 8, 2024.

MPNE consensus 2024 on data, AI and data-dependent business models


Melanoma Patient Network Europe is proud to announce the AI, Data, and Data-Dependent Business Models workshop took place in Berlin between 31 January and 2 February 2024 at Fraunhofer & Heinrich Hertz Institute in the framework of the iToBoS project

iToBoS project in ISE 2024


iToBoS project was present in the Integrated Systems Europe, an international exhibition dedicated to audio-visual and systems integration.

Perspectives for Generative AI at MPNE Consensus on Data Workshop


Presentation of the perspectives of generative Artificial Intelligence in melanoma at the MPNEconsensus 2024 by the iToBoS partner Leibniz University Hannover.

Transformers in Dermoscopic Image Classification


Dermoscopy is a powerful method used in dermatology to analyze the features of skin lesions.

iToBoS project was presented at 27th Annual Meeting European Dermatology Forum


The iToBoS project was presented at 27th Annual Meeting European Dermatology Forum, that took place in Montreux, Switzerland, from 25th to 27th of January, 2024.

Semi-Supervised Learning


Semi-supervised learning is a type of machine learning paradigm that falls between supervised and unsupervised learning.

Assessing and Implementing Trustworthy AI Across Multiple Dimensions


Artificial intelligence (AI) systems have become more and more prevalent in everyday life and especially in enterprise settings.

Melanoma Education with Generative AI in Dermatology


As artificial intelligence (AI) rapidly advances, its integration into dermatology, particularly through Generative Adversarial Networks (GANs), is opening new horizons in patient education and skin cancer diagnosis.