Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels

Logical assessment formula (LAF) [1] was proposed to achieve evaluations with inaccurate ground-truth labels (IAGTLs), which alleviates the usual evaluations with accurate ground-truth labels (AGTLs) [2-6], to assess predictive models for various artificial intelligence applications. LAF is suitable for evaluating the predicted targets of a predictive model in the situation, where the underlying true targets are difficult to be precisely defined while multiple inaccurate targets that contain various information consistent with our prior knowledge about the underlying true target are available. Theoretical analysis of LAF revealed the practicability of LAF for evaluations with IAGTLs, which includes: 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; and 2) LAF can be applied for evaluations with IAGTLs simply from the logical point of view on an easier task, unable to act like usual strategies for evaluations with AGTLs confidently. However, the revealed practicability of LAF for evaluations with IAGTLs has not yet been validated in real-world practice. In this paper, we aimed to address this issue. We applied LAF to tumour segmentation for breast cancer (TSfBC) in medical histopathology whole slide image analysis (MHWSIA). Extensive experiments were conducted and corresponding results and analyses support that the practicability of LAF is valid in the case of TSfBC in MHWSIA, which reflect the potentials of LAF applied to MHWSIA for evaluations with IAGTLs. The rest contents of this paper are structured as follows.