Disease Label

Disease labeling in medical image analysis focuses on accurately assigning disease labels to medical images, often using machine learning to overcome limitations of manual annotation. Current research emphasizes improving label accuracy by addressing issues like noisy data, imbalanced datasets, and the need for incorporating disease severity and uncertainty. This involves developing sophisticated models, including those leveraging transformer architectures, graph neural networks, and hierarchical attention mechanisms, to enhance classification performance and interpretability. Accurate disease labeling is crucial for improving the reliability and clinical utility of AI-powered diagnostic tools.