Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin
lesions at an early stage could aid clinical decision-making by providing an accurate …

Background Melanoma has one of the fastest rising incidence rates of any cancer. It
accounts for a small percentage of skin cancer cases but is responsible for the majority of …

Importance Deep learning convolutional neural networks (CNNs) have shown a
performance at the level of dermatologists in the diagnosis of melanoma. Accordingly …

Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer.
Deep learning methods, mainly convolutional neural networks (CNNs) have recently …

In this article, we describe the design and implementation of a publicly accessible
dermatology image analysis benchmark challenge. The goal of the challenge is to sup-port …

In this paper, we present a novel framework for dermoscopy image recognition via both a
deep learning method and a local descriptor encoding strategy. Specifically, deep …

Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the
development of diagnosis methods for this disease. This paper addresses two different …

Image‐based computer aided diagnosis systems have significant potential for screening
and early detection of malignant melanoma. We review the state of the art in these systems …

The accuracy of the clinical diagnosis of cutaneous melanoma with the unaided eye is only
about 60%. Dermoscopy, a non-invasive, in vivo technique for the microscopic examination …

Background: There is a need for better standardization of the dermoscopic terminology in
assessing pigmented skin lesions. Objective: The virtual Consensus Net Meeting on …