Dr Kovalev and his team specialise in the use of Biomedical Image Analysis and Deep Learning technologies in medical diagnosis and treatment. But the use of large amounts of personal medical data involved in the training of Deep Neural Networks (DNNs) had presented the scientific community with a number of ethical, legal, and security challenges.
So, the Belarussian researchers conducted a number of computational Deep Learning experiments to study the vulnerability of different medical images in order to protect computerised disease diagnosis systems from potential attacks by malicious medical images.
Curious to learn about the result, and how the EaPConnect project supported? Read it on the latest In The Field blog.
