Skin is the largest organ of human body and skin cancer is the most common cancer in the world. Skin cancer is deadly at late stage when cancer spreads to internal organs. However, if being detected early, skin cancer can be cured with high survival rate.
Convolutional neural networks are a type of neural networks often used to perform machine learning techniques on images.
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.
iToBoS project was presented in Actu Toulouse, a digital newspaper that publishes articles on the daily life of the inhabitants of Toulouse and its region, portraits, ideas for outings, tips and practical information. The article is written in French.
Generative Adversarial Networks are unsupervised neural networks that are able to analyse information from a dataset and produce similar new samples.
As observed by Dr Eline Noels of the Erasmus University of Rotterdam, the knowledge of the economic burden of skin cancers is “essential to enable health policy decision-makers to make well-informed decisions on potential interventions and to be able to evaluate the future effect of these decisions”.
For image denoising, there is an important line of work that uses the self-similarity principle that natural images obey: an image contains many image patches similar between each other. To remove noise, one can look for similar patches in an image and average them.
Machine learning and specifically deep learning, have dramatically improved the state-of-the-art in many areas of research, including computer vision, speech recognition, and natural language processing.