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.

Melanoma is the third most common cancer in Australia, but Australians have widely variable risk of developing melanoma. This makes it hard to recommend a one-size-fits-all approach to early detection.

Digital hair removal is a technique that is used to improve the accuracy and reliability of dermoscopic images of melanoma, which is a type of skin cancer.

Most people who know about melanomas have a particular idea of what they look like: dark brown or black blotches or lumps. Many melanomas do look like this, so it’s what many machine learning algorithms are taught to look for.

Backpropagation and rule-based XAI methods are prominent choices to explain neural network predictions. This is due to their speed and efficiency, as the computation of explanations only requires one backward pass through the model.