Risk scores in melanoma detection

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

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.

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.

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.

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.