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.
iToBoS was presented at the American Academy of Dermatology (AAD) Annual Meeting, in New Orleans, USA.
It’s the ethical imperative of medical providers and researchers to improve the health outcomes of either their patients or the general public.
The connection between sun exposure and skin cancers, such as melanoma, is well acknowledged. However, some people are at higher risk of melanoma than others, meaning they may require less sun exposure to cause the DNA damage which can lead to skin cancers.
A recent nation-wide survey of Australian Dermatologists has provided insight into the current use, confidence, attitudes, and education preferences for genetic testing in dermatology practice.
The iToBoS project participated in the Open Innovation Challenge 2023, an event aimed at meeting innovative solutions and technological challenges within the framework of the Mobile World Congress, which took place in Barcelona.
In the last decade the application of artificial intelligence (AI) algorithms in dermatology to classify skin lesions, particularly melanoma, has advanced rapidly. Large international computer skin image analysis challenges have successfully drawn attention to the potential for AI to aid the detection of skin cancers.