Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation
arXiv:2603.26592v2 Announce Type: replace-cross Abstract: Reliable machine-learning models in biomedical settings depend on accurate labels, yet annotating biomedical time-series data remains challenging. Algorithmic sample selection may support annotation, but evidence from studies involving real human annotators is scarce. Consequently, we compare three sample selection methods for annotation: random sampling (RND), farthest-first traversal (FAFT), and a graphical user interface-based method enabling exploration of complementary 2D visualizations (2DVs) of high-dimensional data. We evaluated
The increasing reliance on machine learning in critical biomedical applications makes the efficiency and accuracy of data annotation a pressing concern.
Improving sample selection strategies for biomedical time-series data annotation can significantly accelerate the development of reliable AI models for healthcare.
This research introduces and evaluates interactive visualization as a viable, potentially superior, method for sample selection, moving beyond purely algorithmic approaches.
- · Biomedical AI developers
- · Healthcare diagnostics
- · Patients receiving AI-driven care
- · Inefficient manual annotation processes
- · Purely random sampling methods
More accurate and faster biomedical AI model development.
Reduced costs and accelerated time-to-market for AI-powered health solutions.
Increased adoption of AI in clinical settings due to improved reliability and trust in diagnoses.
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Read at arXiv cs.AI