Evaluations with accurate ground-truth labels (AGTLs) have been widely employed to assess predictive models for artificial intelligence applications. However, in some specific fields, such as medical histopathology whole slide image analysis, it is quite usual the situation that AGTLs are difficult to be precisely defined or even do not exist. To alleviate this situation, we propose logical assessment formula (LAF) and reveal its principles for evaluations with inaccurate AGTLs (IAGTLs). From the revealed principles of LAF, we summarize the practicability of LAF: 1) LAF can be applied for evaluations with IAGTLs on a more difficult task, able to act like usual strategies for evaluations with AGTLs reasonably; 2) LAF can be applied for evaluations with IAGTLs from the logical perspective on an easier task, unable to act like usual strategies for evaluations with AGTLs confidently.
翻译：准确的地面真相标签(AGTLs)的评价被广泛用于评估人工智能应用的预测模型,然而,在某些特定领域,如医学组织病理学全幻灯片图像分析,非常常见的情况是,AGTLs很难准确界定,甚至不存在;为缓解这种情况,我们提出了逻辑评估公式(LAF),并揭示了它用于使用不准确的AGTLs(IAGTLs)进行评估的原则。 根据LAF的公开原则,我们总结了LAF的实用性:1)LAF可以被应用到与IAGTLs一起进行一项更为困难的工作的评价,能够像通常的战略一样,与AGTLs进行合理的评价;2)LAF可以被应用到与IAGTLs一起进行的评价,从逻辑角度看,任务比较容易,不能像AGTLs自信地进行评价的通常战略。
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