Transformers are deep learning architectures created to solve sequence-to-sequence tasks (such as language translation) and proposed in [1].

Just shy over a year ago, the Quantus toolkit v0.1.1 has been shared with the Machine Learning (ML) community as a pre-print on arXiv.org.

Explainable Artificial Intelligence (XAI) can not only be employed to get an insight into the reasoning process of an Artificial Intelligence (AI) model.

The paper ‘Attention Is All You Need’ introduces transformers and the sequence-to-sequence architecture.

Rule-based eXplainable AI (XAI) methods, such as layer-wise relevance propagation (LRP) and DeepLift, provide large flexibility thanks to configurable rules, allowing AI practitioners to tailor the XAI method to the problem at hand.

As we saw in a previous post, some challenges caused in explaining neural network decisions can be overcome via canonization.

iToBoS is aiming to streamline melanoma diagnosis, but what happens once you are diagnosed with melanoma?

Offering a handful of assorted articles and updates, this release offers content about the project, technology and trends.

Optical technologies are a promising tool for the early detection of melanoma, which is a type of skin cancer that can be aggressive and deadly if not detected and treated in its early stages.

Skin cancer is the most common cancer in the world.