Dermatological Disease Datasets

Dermatological disease datasets are crucial for training and evaluating machine learning models for skin disease diagnosis, but their quality and fairness are significant concerns. Current research focuses on improving dataset quality by identifying and correcting errors like mislabeling and duplicates, and mitigating biases related to demographic factors like skin tone, while maintaining patient privacy through federated learning techniques. Researchers employ various deep learning architectures, including convolutional neural networks and vision transformers, often incorporating self-supervised learning and multi-task approaches to enhance model performance and explainability. These advancements aim to improve the accuracy, fairness, and trustworthiness of AI-driven dermatological diagnosis, ultimately leading to better patient care.