Logical assessment formula (LAF) [1] was proposed to alleviate the situation where accurate ground-truth labels are not available for evaluations of an approach for a specific learning task. With a H. pylori segmentation task of medical histopathology whole slide images [1,2], evaluations based on LAF has been qualitatively shown to be able to reflect the logical rationalities of the predictions of various approaches. Comprehensive descriptions of LAF can be found in Section 2. However, the principles of LAF for evaluations without accurate ground-truth labels (AGTL) are not well revealed. In this paper, we provide comprehensive theoretical analyses to reveal the principles of LAF for evaluations without AGTL. Details of the revealed principles of LAF are presented in Section 4. From the revealed principles of LAF, we summarize, for the situation where accurate ground-truth labels are not available while multiple inaccurate targets containing various information consistent with our prior knowledge about the true target are available, the major practicability of LAF is that it can be reasonably applied for evaluations without AGTL on a more difficult task, just acting like usual strategies for evaluations with AGTL; and the minor practicability of LAF is that it can be applied for evaluations without AGTL from the logical perspective on an easier task, unable to be acting like usual strategies for evaluations with AGTL. Details of the practicability of LAF summarized from the revealed principles can be found in Section 5. To verify the practicability of LAF summarized from the revealed principles, we apply LAF on two tumour segmentation tasks in medical histopathology whole slide images (MHWSI) for breast cancer for evaluations without AGTL. Experimental results analyses of LAF applied on tumour segmentation for breast cancer support the practicability of LAF summarized from the revealed principles. Comprehensive contents can be found in Section 6 and 7.

The enthusiastic embracing of AI as the go-to technology for solving specific problems is both undeniable and remarkable. But while there is still much progress being achieved every day through the most popular AI approaches like supervised learning or reinforcement learning, the often monolithic way in which those classic approaches are used may also be the very thing that holds AI back. While AI is increasingly successful in a growing number of fields, it still operates primarily as a tool to execute narrow-focus tasks, or as a simple form of automation, rather than a supporting partner in a relationship with human users. It largely relies on carefully curated or annotated, mostly historical, data, and only very indirectly learns from human users. AI has remarkable predictive power in some cases, yet is incapable of the adaptive prowess routinely demonstrated by humans from their infancy. It simply is not (yet) able to extrapolate on data that it has never encountered quite like humans can.