The logical assessment formula (LAF) is a new theory proposed for evaluations with inaccurate ground-truth labels (IAGTLs) to assess the predictive models for artificial intelligence applications. However, the practicability of LAF for evaluations with IAGTLs has not yet been validated in real-world practice. In this paper, we applied LAF to two tasks of tumour segmentation for breast cancer (TSfBC) in medical histopathology whole slide image analysis (MHWSIA) for evaluations with IAGTLs. Experimental results and analysis show that the LAF-based evaluations with IAGTLs were unable to confidently act like usual evaluations with accurate ground-truth labels on the one easier task of TSfBC while being able to reasonably act like usual evaluations with AGTLs on the other more difficult task of TSfBC. These results and analysis reflect the potential of LAF applied to MHWSIA for evaluations with IAGTLs. This paper presents the first practical validation of LAF for evaluations with IAGTLs in a real-world application.

翻译：逻辑评估公式（LAF）是为应对不精确真实标签（IAGTL）评估而提出的一种新理论，旨在评估人工智能应用中的预测模型。然而，LAF在不精确真实标签评估中的实用性尚未在真实世界实践中得到验证。本文中，我们将LAF应用于医学组织病理学全切片图像分析（MHWSIA）中乳腺癌肿瘤分割（TSfBC）的两个任务，以进行不精确真实标签评估。实验结果表明，在TSfBC中较简单的任务上，基于LAF的不精确真实标签评估无法像常规的精确真实标签（AGTL）评估那样可靠地发挥作用；而在TSfBC中较困难的任务上，它能够合理地近似于常规的精确真实标签评估。这些结果反映了LAF应用于MHWSIA中进行不精确真实标签评估的潜力。本文首次在真实世界应用中验证了LAF用于不精确真实标签评估的实用性。