  Deep learning (DL) enables deep neural networks (DNNs) to automatically learn
complex tasks or rules from given examples without instructions or guiding
principles. As we do not engineer DNNs' functions, it is extremely difficult to
diagnose their decisions, and multiple lines of studies proposed to explain
principles of DNNs/DL operations. Notably, one line of studies suggests that
DNNs may learn concepts, the high level features recognizable to humans. Thus,
we hypothesized that DNNs develop abstract codes, not necessarily recognizable
to humans, which can be used to augment DNNs' decision-making. To address this
hypothesis, we combined foundation segmentation models and unsupervised
learning to extract internal codes and identify potential use of abstract codes
to make DL's decision-making more reliable and safer.
