Validation Metric

Validation metrics are crucial for assessing the reliability and practical applicability of machine learning models, particularly in image analysis, where their appropriate selection significantly impacts scientific progress and clinical translation. Current research emphasizes the need for problem-aware metric selection, focusing on aligning metrics with specific application goals and addressing the variability introduced by different datasets and model hyperparameters. This improved understanding of metric limitations and the development of comprehensive frameworks for metric selection are vital for ensuring the rigor and reproducibility of research across various domains, including biomedical image analysis and model-based optimization.