The scientific work "ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs", with the support of iToBoS project, has been published.
Intégrer l’intelligence artificielle au domaine médical, c’est se poser les bonnes questions, notamment au niveau éthique.
On March 30th, scientists from the International Agency for Research on Cancer published a study on the Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040.
Until a few years ago, regulations and standards existed around the handling and use of certain types of data, including the General Data Protection Regulation (GDPR) in Europe, HIPAA and PCI-DSS in the United States, the Canadian Consumer Privacy Protection Act (CPPA) and many more.
In iToBoS, machine learning/ artificial intelligence is key to combine all the design and make the system really a standout product.
We present an overview of the online datasets meant for machine learning algorithms. As the saying goes, “An algorithm can be only as good as the data set”.
Melanoma has a poor prognosis with median survival of 6-9 months in the absence of timely diagnosis and treatment.
In any data processing project that deals with personal information there is an inherent tradeoff between safeguarding data subjects’ privacy and yielding useful and accurate insights from the data.
The transparency of Artificial Intelligence (AI) models is an essential criterion for the deployment of AI in high-risk settings, such as medical applications. Consequently, numerous approaches for explaining AI systems have been proposed over the years (Samek et al., 2021).