ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

The scientific work "ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs", with the support of iToBoS project, has been published.

[FR] Quelle éthique pour l’intelligence artificielle en santé ?

Intégrer l’intelligence artificielle au domaine médical, c’est se poser les bonnes questions, notamment au niveau éthique. 

Why the early detection of Melanoma will become more important than ever

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.

Outstanding impact of iToBoS project in the media

iToBoS project aims to provide new opportunities and added value to society in terms of novel health solutions, patient care, innovation, technical improvements and economic development.

The changing landscape of AI regulation

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)[1] in Europe, HIPAA[2] and PCI-DSS[3] in the United States, the Canadian Consumer Privacy Protection Act (CPPA)[4] and many more.

Overview of machine learning based approaches for non-contact dermoscopy

In iToBoS, machine learning/ artificial intelligence is key to combine all the design and make the system really a standout product.

Data sets for machine learning based approaches for non-contact dermoscopy

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”. 

The importance of Early Detection in the light of COVID-19

Melanoma has a poor prognosis with median survival of 6-9 months in the absence of timely diagnosis and treatment.

The challenges of infusing privacy and compliance technologies in the iToBoS project

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.

Evaluating AI Explanations with Quantus

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).