Recent years have seen a steep rise in the number of skin cancer detection applications.
While modern advances in deep learning made possible reaching new heights in terms of …

The high performance of machine learning algorithms for the task of skin lesion classification
has been proven over the past few years. However, real-world implementations are still …

Artificial intelligence (AI)-based medical diagnosis has received huge attention due to its
potential to improve and accelerate the decision-making process at the patient level in a …

The incidence of skin cancers has been increasing over the past decades at an alarming
rate. Right now, somewhere in the range of 2 and 3 million non-melanoma skin diseases …

Background One prominent application for deep learning–based classifiers is skin cancer
classification on dermoscopic images. However, classifier evaluation is often limited to …

Background In recent months, multiple publications have demonstrated the use of
convolutional neural networks (CNN) to classify images of skin cancer as precisely as …

Skin cancer is the most common cancer worldwide, with melanoma being the deadliest form.
Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of …

Skin cancer is one of the most common types of cancer worldwide. Over the past few years,
different approaches have been proposed to deal with automated skin cancer detection …

Knowing when a machine learning system is not confident about its prediction is crucial in
medical domains where safety is critical. Ideally, a machine learning algorithm should make …

As skin cancer is one of the most frequent cancers globally, accurate, non-invasive
dermoscopy-based diagnosis becomes essential and promising. A task of the Part 3 of the …