A Unified Approach to Multi-Indicator Evaluation: Opportunities and Challenges

This paper presents a novel approach for evaluating complex systems using multiple indicators. While the method demonstrates a high degree of unity, it presents challenges in retaining the granular information inherent in the original indicators. For example, in clinical significance, the Dice coefficient evaluates algorithm accuracy, while the Hausdorff distance assesses segmentation consistency. The proposed method aims to facilitate comprehensive evaluation by enabling medical professionals to consider the results of multiple indicators and organs simultaneously. Although the method offers enhanced flexibility in selecting multiple accuracy indicators, it remains susceptible to subjectivity. Notably, the manual determination of accuracy index weights during threshold generation introduces variability in final evaluation results, further contributing to subjectivity. This method may encounter limitations when applied to scenarios involving multiple organs. Future research could explore the incorporation of adaptive weight setting techniques to mitigate the influence of subjectivity on evaluation results. Additionally, integrating multi-task learning methods could offer more effective solutions to the segmentation problem of multiple organs. Furthermore, this method's applicability extends beyond medical domains. It can be effectively employed in scenarios involving multi-index evaluation, such as speech recognition performance evaluation, video quality assessment, and user experience-based evaluation.