In a past post, we explained that the biggest limitation of liquid lenses is gravity-induced coma aberrations which cause worse optical performance when the lenses are used with their optical axis oriented differently than vertical.

The decision to integrate liquid lenses into iToBoS’ full-body scanner was taken due to the need to take thousands of pictures in the shortest amount of time.

Diffusion models rival and can even surpass GANs on image synthesis, as they are able to generate more diverse outputs by having a better coverage of the data distribution and do not suffer from mode collapse and training instabilities of GANs.

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?