A Unified Method for Multi-Index Evaluation: Strengths, Limitations, and Future Directions

This paper presents a unified method for multi-index evaluation, achieving a high degree of cohesion while facing the challenge of preserving information from individual indicators. The method employs a comprehensive approach by integrating multiple indicators and organs to aid practitioners in making informed decisions. For example, the Dice coefficient assesses algorithm accuracy in clinical significance, while the Hausdorff distance evaluates segmentation consistency.

While the method offers flexibility in choosing multiple accuracy indicators, potential subjectivity remains due to the manual setting of index weights during threshold generation. This subjective setting may influence the final evaluation results. Furthermore, the method's applicability to multiple organs may be limited by the number of organs considered.

Future research could explore adaptive weight setting methods to minimize subjectivity and enhance evaluation accuracy. Additionally, investigating multi-task learning methods may improve the handling of segmentation challenges involving multiple organs. Notably, the method's applicability extends beyond medical applications, proving beneficial in diverse multi-index evaluation scenarios, such as speech recognition performance, video quality assessment, and user experience-based evaluations.