  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.
