Publicly available skin image datasets are increasingly used to develop machine learning
algorithms for skin cancer diagnosis. However, the total number of datasets and their …

The remarkable success of deep learning has prompted interest in its application to medical
imaging diagnosis. Even though state-of-the-art deep learning models have achieved …

Background and objectives: One principal impediment in the successful deployment of
Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday …

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

Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of
assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of …

Skin cancer, a critical health concern, necessitates accurate early detection and
classification to mitigate its impact. However, the limited availability of medical datasets and …

The limited ability of Convolutional Neural Networks to generalize to images from previously
unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as …

Skin cancer represents one of the most lethal and prevalent types of cancer observed in the
human population. When diagnosed in its early stages, melanoma, a form of skin cancer …

Background A basic requirement for artificial intelligence (AI)–based image analysis
systems, which are to be integrated into clinical practice, is a high robustness. Minor …

Background: While skin cancers are less prevalent in people with skin of color, they are
more often diagnosed at later stages and have a poorer prognosis. The use of artificial …