When melanomas don’t look like melanomas


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.

Improving Explanations with Model Canonization


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 project presented in the AAD Annual Meeting


iToBoS was presented at the American Academy of Dermatology (AAD) Annual Meeting, in New Orleans, USA.

Ethical and governance considerations for genomic data sharing in the development of medical technologies for melanoma


It’s the ethical imperative of medical providers and researchers to improve the health outcomes of either their patients or the general public.

What are the risk factors for melanoma?


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.

Mole matching in iToBoS project


We are all familiar with text search which returns document similar to our query. It is also possible to perform similar search but with images.

Attitudes of Australian dermatologists on the use of genetic testing: A cross-sectional survey with a focus on melanoma


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.

iToBoS in the MWC Open Innovation Challenge 2023


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.

Object detection - part 2


Object detection methods have been developed since early 2000s and continue to grow rapidly until now. The history of object detection can be separated into two eras: traditional detection methods and deep learning based detection methods.

Artificial Intelligence for skin image analysis using Total Body Photography


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.