Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …

For various forms of skin lesion, many different feature extraction methods have been
investigated so far. Indeed, feature extraction is a crucial step in machine learning …

In clinical applications, neural networks must focus on and highlight the most important parts
of an input image. Soft-Attention mechanism enables a neural network to achieve this goal …

Despite the growing availability of high-quality public datasets, the lack of training samples
is still one of the main challenges of deep-learning for skin lesion analysis. Generative …

Abstract Background and Objective: Although automated Skin Lesion Classification (SLC) is
a crucial integral step in computer-aided diagnosis, it remains challenging due to variability …

The human body's major organ is the skin, and it protects human beings from the outside
environment. Detecting skin disease at an earlier stage is a big challenge because of the …

Data-driven models are now deployed in a plethora of real-world applications--including
automated diagnosis--but models learned from data risk learning biases from that same …

Background and objective: According to the National Cancer Institute, among all malignant
tumors, non-melanoma skin cancer, and melanoma are the most frequent in Brazil. Despite …

Pests and diseases are relevant factors for production losses in agriculture and, therefore,
promote a huge investment in the prevention and detection of its causative agents. In many …

Self-supervised pre-training appears as an advantageous alternative to supervised pre-
trained for transfer learning. By synthesizing annotations on pretext tasks, self-supervision …