Image acquisition comes with unavoidable and unwanted noise acquisition due to camera hardware limitations and illumination challenges, making image denoising a fundamental task in image processing.

In the last few years, there have been remarkable developments in computational methods for helping dermatologists to diagnose skin cancer in early stages.

Solving increasingly complex real-world problems, continuously contributes to the success of deep neural networks (DNNs) (Schütt et al. 2017; Senior et al. 2020). DNNs have long been established in numerous machine learning tasks and for this have been significantly improved in the past decade.

The scientific work "Explainable AI Methods - A Brief Overview", with the support of iToBoS project, has been published.

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.

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.

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.

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