Reliable Label-Efficient Learning for Biomedical Image Recognition

The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. To acquire accurate labels as the gold standard, multiple observers with specific expertise are required for both annotation and proofreading. This process can be time-consuming and labor-intensive, making high-quality, and large-annotated biomedical datasets difficult. To address this problem, we propose a deep active learning...