Medical image datasets often exhibit long-tailed distributions due to the inherent challenges
in medical data collection and annotation. In long-tailed contexts, some common disease …

Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex
patient-level conditions and the existence of rare diseases. Existing long-tailed learning …

Due to the difficulty of collecting data samples and the cost of annotation, medical image
datasets often exhibit a long-tailed distribution. In the long-tailed distribution, the head …

Deep learning-based methods have been widely used for medical image classification.
However, in clinical practice, rare diseases are usually underrepresented with limited …

Abstract Background and Objective The incidence rate of skin cancers is increasing
worldwide annually. Using machine learning and deep learning for skin lesion classification …

Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to
detect in the early stages, posing a major threat to human health. In recent years, deep …

Skin image datasets often suffer from imbalanced data distribution, exacerbating the
difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised …

Melanoma is a type of skin cancer with high mortality rate. Early diagnosis of malignant
melanoma usually relies on the judgement of dermatologists. Recently, more and more …

Skin cancer is one of the most widespread cancers which is dangerous and fatal.
Convolutional neural network (CNN) has been widely used in the classification task of …

Skin cancer holds the highest incidence rate among all cancers globally. The importance of
early detection cannot be overstated, as late-stage cases can be lethal. Classifying skin …